diff --git a/.gitignore b/.gitignore
index 088ba6b..7eeb2b7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,4 @@ Cargo.lock
 
 # These are backup files generated by rustfmt
 **/*.rs.bk
+/refs/
diff --git a/Cargo.toml b/Cargo.toml
new file mode 100644
index 0000000..0866e88
--- /dev/null
+++ b/Cargo.toml
@@ -0,0 +1,15 @@
+[package]
+name = "ux-mediapipe"
+version = "0.1.0"
+edition = "2018"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[lib]
+name = "mediapipe"
+
+[dependencies]
+cgmath = "0.18.0"
+libc = "0.2.0"
+opencv = {version = "0.63.0", default-features = false, features = ["videoio", "highgui", "imgproc"]}
+protobuf = "2.23.0"
diff --git a/README.md b/README.md
index c9de18d..dc1b2a5 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,4 @@
-# ux-media
+# ux-mediapipe
 Rust and mediapipe
+
+bazel build --define MEDIAPIPE_DISABLE_GPU=1 mediapipe:mediagraph
\ No newline at end of file
diff --git a/examples/hand_tracking_desktop_live_gpu.txt b/examples/hand_tracking_desktop_live_gpu.txt
new file mode 100644
index 0000000..4dcaac5
--- /dev/null
+++ b/examples/hand_tracking_desktop_live_gpu.txt
@@ -0,0 +1,48 @@
+# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/handtrackinggpu.
+
+# GPU image. (GpuBuffer)
+input_stream: "input_video"
+
+# GPU image. (GpuBuffer)
+output_stream: "output_video"
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "hand_landmarks"
+
+# Generates side packet cotaining max number of hands to detect/track.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:num_hands"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 2 }
+    }
+  }
+}
+
+# Detects/tracks hand landmarks.
+node {
+  calculator: "HandLandmarkTrackingGpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_HANDS:num_hands"
+  output_stream: "LANDMARKS:hand_landmarks"
+  output_stream: "HANDEDNESS:handedness"
+  output_stream: "PALM_DETECTIONS:palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects_from_landmarks"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see hand_renderer_gpu.pbtxt).
+node {
+  calculator: "HandRendererSubgraph"
+  input_stream: "IMAGE:input_video"
+  input_stream: "DETECTIONS:palm_detections"
+  input_stream: "LANDMARKS:hand_landmarks"
+  input_stream: "HANDEDNESS:handedness"
+  input_stream: "NORM_RECTS:0:hand_rects_from_palm_detections"
+  input_stream: "NORM_RECTS:1:hand_rects_from_landmarks"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/examples/hello.rs b/examples/hello.rs
new file mode 100644
index 0000000..327e42e
--- /dev/null
+++ b/examples/hello.rs
@@ -0,0 +1,212 @@
+#![allow(unused_variables)]
+#![allow(dead_code)]
+
+use mediapipe::*;
+
+mod examples {
+    use super::*;
+    use opencv::prelude::*;
+    use opencv::{highgui, imgproc, videoio, Result};
+
+    pub fn corner_rectangle() -> Result<()> {
+        let window = "video capture";
+
+        highgui::named_window(window, highgui::WINDOW_AUTOSIZE)?;
+
+        let mut cap = videoio::VideoCapture::new(0, videoio::CAP_ANY)?;
+        if !cap.is_opened()? {
+            panic!("Unable to open default cam")
+        }
+
+        let detector = hands::HandDetector::default();
+
+        loop {
+            let mut frame = Mat::default();
+            cap.read(&mut frame)?;
+            let size = frame.size()?;
+            if size.width > 0 {
+                highgui::imshow(window, &mut frame)?
+            }
+            let key = highgui::wait_key(10)?;
+            if key > 0 && key != 255 {
+                break;
+            }
+        }
+        Ok(())
+    }
+
+    // pub fn face_detection() -> Result<()> {
+    //     let window = "video capture";
+
+    //     highgui::named_window(window, highgui::WINDOW_AUTOSIZE)?;
+
+    //     let mut cap = videoio::VideoCapture::new(0, videoio::CAP_ANY)?;
+    //     if !cap.is_opened()? {
+    //         panic!("Unable to open default cam")
+    //     }
+
+    //     let detector = mediapipe::face_detection::FaceDetector::default();
+
+    //     loop {
+    //         let mut frame = Mat::default();
+    //         cap.read(&mut frame)?;
+    //         let size = frame.size()?;
+    //         if size.width > 0 {
+    //             highgui::imshow(window, &mut frame)?
+    //         }
+    //         let key = highgui::wait_key(10)?;
+    //         if key > 0 && key != 255 {
+    //             break;
+    //         }
+    //     }
+    //     Ok(())
+    // }
+
+    pub fn face_mesh() -> Result<()> {
+        let window = "video capture";
+
+        highgui::named_window(window, highgui::WINDOW_AUTOSIZE)?;
+
+        let mut cap = videoio::VideoCapture::new(0, videoio::CAP_ANY)?;
+        if !cap.is_opened()? {
+            panic!("Unable to open default cam")
+        }
+
+        cap.set(videoio::CAP_PROP_FRAME_WIDTH, 640.0)?;
+        cap.set(videoio::CAP_PROP_FRAME_HEIGHT, 480.0)?;
+        cap.set(videoio::CAP_PROP_FPS, 30.0)?;
+
+        let mut mesh = FaceMesh::default();
+        let mut detector = face_mesh::FaceMeshDetector::default();
+
+        let mut raw_frame = Mat::default();
+        let mut rgb_frame = Mat::default();
+        let mut flip_frame = Mat::default();
+        loop {
+            cap.read(&mut raw_frame)?;
+
+            let size = raw_frame.size()?;
+            if size.width > 0 && !raw_frame.empty() {
+                imgproc::cvt_color(&raw_frame, &mut rgb_frame, imgproc::COLOR_BGR2RGB, 0)?;
+                opencv::core::flip(&rgb_frame, &mut flip_frame, 1)?; // horizontal
+
+                detector.process(&flip_frame, &mut mesh);
+
+                highgui::imshow(window, &mut flip_frame)?;
+                println!(
+                    "LANDMARK: {} {} {}",
+                    mesh.data[0].x, mesh.data[0].y, mesh.data[0].z
+                );
+            } else {
+                println!("WARN: Skip empty frame");
+            }
+
+            let key = highgui::wait_key(10)?;
+            if key > 0 && key != 255 {
+                break;
+            }
+        }
+        Ok(())
+    }
+
+    pub fn hand_tracking() -> Result<()> {
+        let window = "video capture";
+
+        highgui::named_window(window, highgui::WINDOW_AUTOSIZE)?;
+
+        let mut cap = videoio::VideoCapture::new(0, videoio::CAP_ANY)?;
+        if !cap.is_opened()? {
+            panic!("Unable to open default cam")
+        }
+
+        cap.set(videoio::CAP_PROP_FRAME_WIDTH, 640.0)?;
+        cap.set(videoio::CAP_PROP_FRAME_HEIGHT, 480.0)?;
+        cap.set(videoio::CAP_PROP_FPS, 30.0)?;
+
+        let mut left = Hand::default();
+        let mut right = Hand::default();
+        let mut detector = hands::HandDetector::default();
+
+        let mut raw_frame = Mat::default();
+        let mut rgb_frame = Mat::default();
+        let mut flip_frame = Mat::default();
+        loop {
+            cap.read(&mut raw_frame)?;
+
+            let size = raw_frame.size()?;
+            if size.width > 0 && !raw_frame.empty() {
+                imgproc::cvt_color(&raw_frame, &mut rgb_frame, imgproc::COLOR_BGR2RGB, 0)?;
+                opencv::core::flip(&rgb_frame, &mut flip_frame, 1)?; // horizontal
+                
+                detector.process(&flip_frame, &mut left, &mut right);
+
+                highgui::imshow(window, &mut flip_frame)?;
+                println!(
+                    "LANDMARK: {} {} {}",
+                    left.data[0].x, left.data[0].y, left.data[0].z
+                );
+            } else {
+                println!("WARN: Skip empty frame");
+            }
+
+            let key = highgui::wait_key(10)?;
+            if key > 0 && key != 255 {
+                break;
+            }
+        }
+        Ok(())
+    }
+
+    pub fn pose_estimation() -> Result<()> {
+        let window = "video capture";
+
+        highgui::named_window(window, highgui::WINDOW_AUTOSIZE)?;
+
+        let mut cap = videoio::VideoCapture::new(0, videoio::CAP_ANY)?;
+        if !cap.is_opened()? {
+            panic!("Unable to open default cam")
+        }
+
+        cap.set(videoio::CAP_PROP_FRAME_WIDTH, 640.0)?;
+        cap.set(videoio::CAP_PROP_FRAME_HEIGHT, 480.0)?;
+        cap.set(videoio::CAP_PROP_FPS, 30.0)?;
+
+        let mut pose = Pose::default();
+        let mut detector = pose::PoseDetector::default();
+
+        let mut raw_frame = Mat::default();
+        let mut rgb_frame = Mat::default();
+        let mut flip_frame = Mat::default();
+        loop {
+            cap.read(&mut raw_frame)?;
+
+            let size = raw_frame.size()?;
+            if size.width > 0 && !raw_frame.empty() {
+                imgproc::cvt_color(&raw_frame, &mut rgb_frame, imgproc::COLOR_BGR2RGB, 0)?;
+                opencv::core::flip(&rgb_frame, &mut flip_frame, 1)?; // horizontal
+
+                detector.process(&rgb_frame, &mut pose);
+
+                highgui::imshow(window, &mut rgb_frame)?;
+                println!(
+                    "LANDMARK: {} {} {}",
+                    pose.data[0].x, pose.data[0].y, pose.data[0].z
+                );
+            } else {
+                println!("WARN: Skip empty frame");
+            }
+
+            let key = highgui::wait_key(10)?;
+            if key > 0 && key != 255 {
+                break;
+            }
+        }
+        Ok(())
+    }
+}
+
+fn main() {
+    // examples::pose_estimation().unwrap()
+    // examples::hand_tracking().unwrap()
+    examples::face_mesh().unwrap()
+}
diff --git a/mediapipe/graphs/edge_detection/BUILD b/mediapipe/graphs/edge_detection/BUILD
new file mode 100644
index 0000000..fac2411
--- /dev/null
+++ b/mediapipe/graphs/edge_detection/BUILD
@@ -0,0 +1,36 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/image:luminance_calculator",
+        "//mediapipe/calculators/image:sobel_edges_calculator",
+    ],
+)
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+mediapipe_binary_graph(
+    name = "mobile_gpu_binary_graph",
+    graph = "edge_detection_mobile_gpu.pbtxt",
+    output_name = "mobile_gpu.binarypb",
+)
diff --git a/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt b/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
new file mode 100644
index 0000000..e3c572e
--- /dev/null
+++ b/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
@@ -0,0 +1,22 @@
+# MediaPipe graph that performs GPU Sobel edge detection on a live video stream.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic:helloworld
+# and mediapipe/examples/ios/helloworld.
+
+# Images coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Converts RGB images into luminance images, still stored in RGB format.
+node: {
+  calculator: "LuminanceCalculator"
+  input_stream: "input_video"
+  output_stream: "luma_video"
+}
+
+# Applies the Sobel filter to luminance images stored in RGB format.
+node: {
+  calculator: "SobelEdgesCalculator"
+  input_stream: "luma_video"
+  output_stream: "output_video"
+}
diff --git a/mediapipe/graphs/face_detection/BUILD b/mediapipe/graphs/face_detection/BUILD
new file mode 100644
index 0000000..9e7cf25
--- /dev/null
+++ b/mediapipe/graphs/face_detection/BUILD
@@ -0,0 +1,95 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
+        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
+        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
+    ],
+)
+
+cc_library(
+    name = "desktop_live_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
+    ],
+)
+
+cc_library(
+    name = "desktop_live_gpu_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "face_detection_mobile_cpu_binary_graph",
+    graph = "face_detection_mobile_cpu.pbtxt",
+    output_name = "face_detection_mobile_cpu.binarypb",
+    deps = [":mobile_calculators"],
+)
+
+mediapipe_binary_graph(
+    name = "face_detection_mobile_gpu_binary_graph",
+    graph = "face_detection_mobile_gpu.pbtxt",
+    output_name = "face_detection_mobile_gpu.binarypb",
+    deps = [":mobile_calculators"],
+)
+
+cc_library(
+    name = "face_detection_full_range_mobile_gpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/modules/face_detection:face_detection_full_range_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "face_detection_full_range_mobile_gpu_binary_graph",
+    graph = "face_detection_full_range_mobile_gpu.pbtxt",
+    output_name = "face_detection_full_range_mobile_gpu.binarypb",
+    deps = [":face_detection_full_range_mobile_gpu_deps"],
+)
+
+cc_library(
+    name = "face_detection_full_range_desktop_live_deps",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/modules/face_detection:face_detection_full_range_cpu",
+    ],
+)
diff --git a/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt b/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt
new file mode 100644
index 0000000..023bea9
--- /dev/null
+++ b/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt
@@ -0,0 +1,58 @@
+# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
+
+# CPU buffer. (ImageFrame)
+input_stream: "input_video"
+
+# Output image with rendered results. (ImageFrame)
+output_stream: "output_video"
+# Detected faces. (std::vector<Detection>)
+output_stream: "face_detections"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Subgraph that detects faces.
+node {
+  calculator: "FaceDetectionShortRangeCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:face_detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/face_detection/face_detection_full_range_desktop_live.pbtxt b/mediapipe/graphs/face_detection/face_detection_full_range_desktop_live.pbtxt
new file mode 100644
index 0000000..0fdb6b9
--- /dev/null
+++ b/mediapipe/graphs/face_detection/face_detection_full_range_desktop_live.pbtxt
@@ -0,0 +1,60 @@
+# MediaPipe graph that performs face detection with TensorFlow Lite on CPU.
+# Used in the examples in
+# mediapipe/examples/desktop/face_detection:face_detection_cpu.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
+# generating the corresponding detections before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionFullRangeCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
+
diff --git a/mediapipe/graphs/face_detection/face_detection_full_range_mobile_gpu.pbtxt b/mediapipe/graphs/face_detection/face_detection_full_range_mobile_gpu.pbtxt
new file mode 100644
index 0000000..8224543
--- /dev/null
+++ b/mediapipe/graphs/face_detection/face_detection_full_range_mobile_gpu.pbtxt
@@ -0,0 +1,60 @@
+# MediaPipe graph that performs face detection with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipie/examples/android/src/java/com/mediapipe/apps/facedetectiongpu and
+# mediapipie/examples/ios/facedetectiongpu.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
+# generating the corresponding detections before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionFullRangeGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE_GPU:output_video"
+}
diff --git a/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt b/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt
new file mode 100644
index 0000000..681d2db
--- /dev/null
+++ b/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt
@@ -0,0 +1,76 @@
+# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# Output image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+# Detected faces. (std::vector<Detection>)
+output_stream: "face_detections"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Transfers the input image from GPU to CPU memory for the purpose of
+# demonstrating a CPU-based pipeline. Note that the input image on GPU has the
+# origin defined at the bottom-left corner (OpenGL convention). As a result,
+# the transferred image on CPU also shares the same representation.
+node: {
+  calculator: "GpuBufferToImageFrameCalculator"
+  input_stream: "throttled_input_video"
+  output_stream: "input_video_cpu"
+}
+
+# Subgraph that detects faces.
+node {
+  calculator: "FaceDetectionShortRangeCpu"
+  input_stream: "IMAGE:input_video_cpu"
+  output_stream: "DETECTIONS:face_detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_video_cpu"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video_cpu"
+}
+
+# Transfers the annotated image from CPU back to GPU memory, to be sent out of
+# the graph.
+node: {
+  calculator: "ImageFrameToGpuBufferCalculator"
+  input_stream: "output_video_cpu"
+  output_stream: "output_video"
+}
diff --git a/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt b/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt
new file mode 100644
index 0000000..d235d1c
--- /dev/null
+++ b/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt
@@ -0,0 +1,58 @@
+# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# Output image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+# Detected faces. (std::vector<Detection>)
+output_stream: "face_detections"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Subgraph that detects faces.
+node {
+  calculator: "FaceDetectionShortRangeGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:face_detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE_GPU:output_video"
+}
diff --git a/mediapipe/graphs/face_effect/BUILD b/mediapipe/graphs/face_effect/BUILD
new file mode 100644
index 0000000..69d648e
--- /dev/null
+++ b/mediapipe/graphs/face_effect/BUILD
@@ -0,0 +1,44 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "face_effect_gpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:immediate_mux_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/framework/tool:switch_container",
+        "//mediapipe/graphs/face_effect/subgraphs:single_face_geometry_from_detection_gpu",
+        "//mediapipe/graphs/face_effect/subgraphs:single_face_geometry_from_landmarks_gpu",
+        "//mediapipe/modules/face_geometry:effect_renderer_calculator",
+        "//mediapipe/modules/face_geometry:env_generator_calculator",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "face_effect_gpu_binary_graph",
+    graph = "face_effect_gpu.pbtxt",
+    output_name = "face_effect_gpu.binarypb",
+    deps = [":face_effect_gpu_deps"],
+)
diff --git a/mediapipe/graphs/face_effect/data/BUILD b/mediapipe/graphs/face_effect/data/BUILD
new file mode 100644
index 0000000..9993699
--- /dev/null
+++ b/mediapipe/graphs/face_effect/data/BUILD
@@ -0,0 +1,47 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework:encode_binary_proto.bzl", "encode_binary_proto")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+encode_binary_proto(
+    name = "axis",
+    input = "axis.pbtxt",
+    message_type = "mediapipe.face_geometry.Mesh3d",
+    output = "axis.binarypb",
+    deps = [
+        "//mediapipe/modules/face_geometry/protos:mesh_3d_proto",
+    ],
+)
+
+encode_binary_proto(
+    name = "glasses",
+    input = "glasses.pbtxt",
+    message_type = "mediapipe.face_geometry.Mesh3d",
+    output = "glasses.binarypb",
+    deps = [
+        "//mediapipe/modules/face_geometry/protos:mesh_3d_proto",
+    ],
+)
+
+# `.pngblob` is used instead of `.png` to prevent iOS build from preprocessing the image.
+# OpenCV is unable to read a PNG file preprocessed by the iOS build.
+exports_files([
+    "axis.pngblob",
+    "facepaint.pngblob",
+    "glasses.pngblob",
+])
diff --git a/mediapipe/graphs/face_effect/data/axis.pbtxt b/mediapipe/graphs/face_effect/data/axis.pbtxt
new file mode 100644
index 0000000..6a3fd52
--- /dev/null
+++ b/mediapipe/graphs/face_effect/data/axis.pbtxt
@@ -0,0 +1,320 @@
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diff --git a/mediapipe/graphs/face_effect/data/axis.pngblob b/mediapipe/graphs/face_effect/data/axis.pngblob
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diff --git a/mediapipe/graphs/face_effect/data/facepaint.pngblob b/mediapipe/graphs/face_effect/data/facepaint.pngblob
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diff --git a/mediapipe/graphs/face_effect/data/glasses.pbtxt b/mediapipe/graphs/face_effect/data/glasses.pbtxt
new file mode 100644
index 0000000..dcfef52
--- /dev/null
+++ b/mediapipe/graphs/face_effect/data/glasses.pbtxt
@@ -0,0 +1,27815 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
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diff --git a/mediapipe/graphs/face_effect/data/glasses.pngblob b/mediapipe/graphs/face_effect/data/glasses.pngblob
new file mode 100644
index 0000000..865584b
Binary files /dev/null and b/mediapipe/graphs/face_effect/data/glasses.pngblob differ
diff --git a/mediapipe/graphs/face_effect/face_effect_gpu.pbtxt b/mediapipe/graphs/face_effect/face_effect_gpu.pbtxt
new file mode 100644
index 0000000..40888d0
--- /dev/null
+++ b/mediapipe/graphs/face_effect/face_effect_gpu.pbtxt
@@ -0,0 +1,130 @@
+# MediaPipe graph that applies a face effect to the input video stream.
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# An integer, which indicate which effect is selected. (int)
+#
+# If `selected_effect_id` is `0`, the Axis effect is selected.
+# If `selected_effect_id` is `1`, the Facepaint effect is selected.
+# If `selected_effect_id` is `2`, the Glasses effect is selected.
+#
+# No other values are allowed for `selected_effect_id`.
+input_stream: "selected_effect_id"
+
+# Indicates whether to use the face detection as the input source. (bool)
+#
+# If `true`, the face detection pipeline will be used to produce landmarks.
+# If `false`, the face landmark pipeline will be used to produce landmarks.
+input_side_packet: "use_face_detection_input_source"
+
+# Output image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+
+# A list of geometry data for a single detected face.
+#
+# NOTE: there will not be an output packet in this stream for this particular
+# timestamp if none of faces detected.
+#
+# (std::vector<face_geometry::FaceGeometry>)
+output_stream: "multi_face_geometry"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Generates an environment that describes the current virtual scene.
+node {
+  calculator: "FaceGeometryEnvGeneratorCalculator"
+  output_side_packet: "ENVIRONMENT:environment"
+  node_options: {
+    [type.googleapis.com/mediapipe.FaceGeometryEnvGeneratorCalculatorOptions] {
+      environment: {
+        origin_point_location: TOP_LEFT_CORNER
+        perspective_camera: {
+          vertical_fov_degrees: 63.0  # 63 degrees
+          near: 1.0  # 1cm
+          far: 10000.0  # 100m
+        }
+      }
+    }
+  }
+}
+
+# Computes the face geometry for a single face. The input source is defined
+# through `use_face_detection_input_source`.
+node {
+  calculator: "SwitchContainer"
+  input_stream: "IMAGE:throttled_input_video"
+  input_side_packet: "ENABLE:use_face_detection_input_source"
+  input_side_packet: "ENVIRONMENT:environment"
+  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+  node_options: {
+    [type.googleapis.com/mediapipe.SwitchContainerOptions] {
+      contained_node: {
+        calculator: "SingleFaceGeometryFromLandmarksGpu"
+      }
+      contained_node: {
+        calculator: "SingleFaceGeometryFromDetectionGpu"
+      }
+    }
+  }
+}
+
+# Renders the selected effect based on `selected_effect_id`.
+node {
+  calculator: "SwitchContainer"
+  input_stream: "SELECT:selected_effect_id"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+  input_side_packet: "ENVIRONMENT:environment"
+  output_stream: "IMAGE_GPU:output_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.SwitchContainerOptions] {
+      contained_node: {
+        calculator: "FaceGeometryEffectRendererCalculator"
+        node_options: {
+          [type.googleapis.com/mediapipe.FaceGeometryEffectRendererCalculatorOptions] {
+            effect_texture_path: "mediapipe/graphs/face_effect/data/axis.pngblob"
+            effect_mesh_3d_path: "mediapipe/graphs/face_effect/data/axis.binarypb"
+          }
+        }
+      }
+      contained_node: {
+        calculator: "FaceGeometryEffectRendererCalculator"
+        node_options: {
+          [type.googleapis.com/mediapipe.FaceGeometryEffectRendererCalculatorOptions] {
+            effect_texture_path: "mediapipe/graphs/face_effect/data/facepaint.pngblob"
+          }
+        }
+      }
+      contained_node: {
+        calculator: "FaceGeometryEffectRendererCalculator"
+        node_options: {
+          [type.googleapis.com/mediapipe.FaceGeometryEffectRendererCalculatorOptions] {
+            effect_texture_path: "mediapipe/graphs/face_effect/data/glasses.pngblob"
+            effect_mesh_3d_path: "mediapipe/graphs/face_effect/data/glasses.binarypb"
+          }
+        }
+      }
+    }
+  }
+}
+
diff --git a/mediapipe/graphs/face_effect/subgraphs/BUILD b/mediapipe/graphs/face_effect/subgraphs/BUILD
new file mode 100644
index 0000000..0b23ad5
--- /dev/null
+++ b/mediapipe/graphs/face_effect/subgraphs/BUILD
@@ -0,0 +1,61 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "face_landmarks_smoothing",
+    graph = "face_landmarks_smoothing.pbtxt",
+    register_as = "FaceLandmarksSmoothing",
+    deps = [
+        "//mediapipe/calculators/util:landmarks_smoothing_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "single_face_geometry_from_detection_gpu",
+    graph = "single_face_geometry_from_detection_gpu.pbtxt",
+    register_as = "SingleFaceGeometryFromDetectionGpu",
+    deps = [
+        ":face_landmarks_smoothing",
+        "//mediapipe/calculators/core:concatenate_detection_vector_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
+        "//mediapipe/modules/face_geometry:face_geometry_from_detection",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "single_face_geometry_from_landmarks_gpu",
+    graph = "single_face_geometry_from_landmarks_gpu.pbtxt",
+    register_as = "SingleFaceGeometryFromLandmarksGpu",
+    deps = [
+        ":face_landmarks_smoothing",
+        "//mediapipe/calculators/core:concatenate_vector_calculator",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:landmarks_smoothing_calculator",
+        "//mediapipe/modules/face_geometry:face_geometry_from_landmarks",
+        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
+    ],
+)
diff --git a/mediapipe/graphs/face_effect/subgraphs/face_landmarks_smoothing.pbtxt b/mediapipe/graphs/face_effect/subgraphs/face_landmarks_smoothing.pbtxt
new file mode 100644
index 0000000..3f565f5
--- /dev/null
+++ b/mediapipe/graphs/face_effect/subgraphs/face_landmarks_smoothing.pbtxt
@@ -0,0 +1,24 @@
+# MediaPipe subgraph that smoothes face landmarks.
+
+type: "FaceLandmarksSmoothing"
+
+input_stream: "NORM_LANDMARKS:landmarks"
+input_stream: "IMAGE_SIZE:input_image_size"
+output_stream: "NORM_FILTERED_LANDMARKS:filtered_landmarks"
+
+# Applies smoothing to a face landmark list. The filter options were handpicked
+# to achieve better visual results.
+node {
+  calculator: "LandmarksSmoothingCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  input_stream: "IMAGE_SIZE:input_image_size"
+  output_stream: "NORM_FILTERED_LANDMARKS:filtered_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksSmoothingCalculatorOptions] {
+      velocity_filter: {
+        window_size: 5
+        velocity_scale: 20.0
+      }
+    }
+  }
+}
diff --git a/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_detection_gpu.pbtxt b/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_detection_gpu.pbtxt
new file mode 100644
index 0000000..bce72c1
--- /dev/null
+++ b/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_detection_gpu.pbtxt
@@ -0,0 +1,91 @@
+# MediaPipe subgraph that extracts geometry from a single face using the face
+# landmark pipeline on an input GPU image. The face landmarks are also
+# "smoothed" to achieve better visual results.
+
+type: "SingleFaceGeometryFromDetectionGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:input_image"
+
+# Environment that describes the current virtual scene.
+# (face_geometry::Environment)
+input_side_packet: "ENVIRONMENT:environment"
+
+# A list of geometry data for a single detected face. The size of this
+# collection is at most 1 because of the single-face use in this graph.
+# (std::vector<face_geometry::FaceGeometry>)
+#
+# NOTE: if no face is detected at a particular timestamp, there will not be an
+# output packet in the `MULTI_FACE_GEOMETRY` stream for this timestamp. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+
+# Subgraph that detects faces and corresponding landmarks using the face
+# detection pipeline.
+node {
+  calculator: "FaceDetectionShortRangeGpu"
+  input_stream: "IMAGE:input_image"
+  output_stream: "DETECTIONS:multi_face_detection"
+}
+
+# Extracts the first face detection associated with the most prominent face from
+# a collection.
+node {
+  calculator: "SplitDetectionVectorCalculator"
+  input_stream: "multi_face_detection"
+  output_stream: "face_detection"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Extracts face detection keypoints as a normalized landmarks.
+node {
+  calculator: "DetectionToLandmarksCalculator"
+  input_stream: "DETECTION:face_detection"
+  output_stream: "LANDMARKS:face_landmarks"
+}
+
+# Extracts the input image frame dimensions as a separate packet.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  output_stream: "SIZE:input_image_size"
+}
+
+# Applies smoothing to the face landmarks previously extracted from the face
+# detection keypoints.
+node {
+  calculator: "FaceLandmarksSmoothing"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  input_stream: "IMAGE_SIZE:input_image_size"
+  output_stream: "NORM_FILTERED_LANDMARKS:smoothed_face_landmarks"
+}
+
+# Converts smoothed face landmarks back into the detection format.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:smoothed_face_landmarks"
+  output_stream: "DETECTION:smoothed_face_detection"
+}
+
+# Puts the smoothed single face detection back into a collection to simplify
+# passing the result into the `FaceGeometryFromDetection` subgraph.
+node {
+  calculator: "ConcatenateDetectionVectorCalculator"
+  input_stream: "smoothed_face_detection"
+  output_stream: "multi_smoothed_face_detection"
+}
+
+# Computes face geometry from the single face detection.
+node {
+  calculator: "FaceGeometryFromDetection"
+  input_stream: "MULTI_FACE_DETECTION:multi_smoothed_face_detection"
+  input_stream: "IMAGE_SIZE:input_image_size"
+  input_side_packet: "ENVIRONMENT:environment"
+  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+}
diff --git a/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_landmarks_gpu.pbtxt b/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_landmarks_gpu.pbtxt
new file mode 100644
index 0000000..364e386
--- /dev/null
+++ b/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_landmarks_gpu.pbtxt
@@ -0,0 +1,89 @@
+# MediaPipe subgraph that extracts geometry from a single face using the face
+# landmark pipeline on an input GPU image. The face landmarks are also
+# "smoothed" to achieve better visual results.
+
+type: "SingleFaceGeometryFromLandmarksGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:input_image"
+
+# Environment that describes the current virtual scene.
+# (face_geometry::Environment)
+input_side_packet: "ENVIRONMENT:environment"
+
+# A list of geometry data for a single detected face. The size of this
+# collection is at most 1 because of the single-face use in this graph.
+# (std::vector<face_geometry::FaceGeometry>)
+#
+# NOTE: if no face is detected at a particular timestamp, there will not be an
+# output packet in the `MULTI_FACE_GEOMETRY` stream for this timestamp. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+
+# Creates a packet to inform the `FaceLandmarkFrontGpu` subgraph to detect at
+# most 1 face.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:num_faces"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+    }
+  }
+}
+
+# Subgraph that detects faces and corresponding landmarks using the face
+# landmark pipeline.
+node {
+  calculator: "FaceLandmarkFrontGpu"
+  input_stream: "IMAGE:input_image"
+  input_side_packet: "NUM_FACES:num_faces"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+}
+
+# Extracts a single set of face landmarks associated with the most prominent
+# face detected from a collection.
+node {
+  calculator: "SplitNormalizedLandmarkListVectorCalculator"
+  input_stream: "multi_face_landmarks"
+  output_stream: "face_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Extracts the input image frame dimensions as a separate packet.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  output_stream: "SIZE:input_image_size"
+}
+
+# Applies smoothing to the single set of face landmarks.
+node {
+  calculator: "FaceLandmarksSmoothing"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  input_stream: "IMAGE_SIZE:input_image_size"
+  output_stream: "NORM_FILTERED_LANDMARKS:smoothed_face_landmarks"
+}
+
+# Puts the single set of smoothed landmarks back into a collection to simplify
+# passing the result into the `FaceGeometryFromLandmarks` subgraph.
+node {
+  calculator: "ConcatenateLandmarListVectorCalculator"
+  input_stream: "smoothed_face_landmarks"
+  output_stream: "multi_smoothed_face_landmarks"
+}
+
+# Computes face geometry from face landmarks for a single face.
+node {
+  calculator: "FaceGeometryFromLandmarks"
+  input_stream: "MULTI_FACE_LANDMARKS:multi_smoothed_face_landmarks"
+  input_stream: "IMAGE_SIZE:input_image_size"
+  input_side_packet: "ENVIRONMENT:environment"
+  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+}
diff --git a/mediapipe/graphs/face_mesh/BUILD b/mediapipe/graphs/face_mesh/BUILD
new file mode 100644
index 0000000..6926fda
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/BUILD
@@ -0,0 +1,69 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "desktop_calculators",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_cpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
+    ],
+)
+
+cc_library(
+    name = "desktop_live_calculators",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_cpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
+    ],
+)
+
+cc_library(
+    name = "desktop_live_gpu_calculators",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_gpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
+    ],
+)
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_gpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "face_mesh_mobile_gpu_binary_graph",
+    graph = "face_mesh_mobile.pbtxt",
+    output_name = "face_mesh_mobile_gpu.binarypb",
+    deps = [":mobile_calculators"],
+)
diff --git a/mediapipe/graphs/face_mesh/calculators/BUILD b/mediapipe/graphs/face_mesh/calculators/BUILD
new file mode 100644
index 0000000..3bebfc9
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/calculators/BUILD
@@ -0,0 +1,37 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "face_landmarks_to_render_data_calculator",
+    srcs = ["face_landmarks_to_render_data_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_options_cc_proto",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/formats:location_data_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/util:color_cc_proto",
+        "//mediapipe/util:render_data_cc_proto",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings",
+    ],
+    alwayslink = 1,
+)
diff --git a/mediapipe/graphs/face_mesh/calculators/face_landmarks_to_render_data_calculator.cc b/mediapipe/graphs/face_mesh/calculators/face_landmarks_to_render_data_calculator.cc
new file mode 100644
index 0000000..093a732
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/calculators/face_landmarks_to_render_data_calculator.cc
@@ -0,0 +1,104 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "absl/memory/memory.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/str_join.h"
+#include "mediapipe/calculators/util/landmarks_to_render_data_calculator.h"
+#include "mediapipe/calculators/util/landmarks_to_render_data_calculator.pb.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_options.pb.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/formats/location_data.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/util/color.pb.h"
+#include "mediapipe/util/render_data.pb.h"
+namespace mediapipe {
+
+namespace {
+
+constexpr int kNumFaceLandmarkConnections = 132;
+// Pairs of landmark indices to be rendered with connections.
+constexpr int kFaceLandmarkConnections[] = {
+    // Lips.
+    61, 146, 146, 91, 91, 181, 181, 84, 84, 17, 17, 314, 314, 405, 405, 321,
+    321, 375, 375, 291, 61, 185, 185, 40, 40, 39, 39, 37, 37, 0, 0, 267, 267,
+    269, 269, 270, 270, 409, 409, 291, 78, 95, 95, 88, 88, 178, 178, 87, 87, 14,
+    14, 317, 317, 402, 402, 318, 318, 324, 324, 308, 78, 191, 191, 80, 80, 81,
+    81, 82, 82, 13, 13, 312, 312, 311, 311, 310, 310, 415, 415, 308,
+    // Left eye.
+    33, 7, 7, 163, 163, 144, 144, 145, 145, 153, 153, 154, 154, 155, 155, 133,
+    33, 246, 246, 161, 161, 160, 160, 159, 159, 158, 158, 157, 157, 173, 173,
+    133,
+    // Left eyebrow.
+    46, 53, 53, 52, 52, 65, 65, 55, 70, 63, 63, 105, 105, 66, 66, 107,
+    // Left iris.
+    474, 475, 475, 476, 476, 477, 477, 474,
+    // Right eye.
+    263, 249, 249, 390, 390, 373, 373, 374, 374, 380, 380, 381, 381, 382, 382,
+    362, 263, 466, 466, 388, 388, 387, 387, 386, 386, 385, 385, 384, 384, 398,
+    398, 362,
+    // Right eyebrow.
+    276, 283, 283, 282, 282, 295, 295, 285, 300, 293, 293, 334, 334, 296, 296,
+    336,
+    // Right iris.
+    469, 470, 470, 471, 471, 472, 472, 469,
+    // Face oval.
+    10, 338, 338, 297, 297, 332, 332, 284, 284, 251, 251, 389, 389, 356, 356,
+    454, 454, 323, 323, 361, 361, 288, 288, 397, 397, 365, 365, 379, 379, 378,
+    378, 400, 400, 377, 377, 152, 152, 148, 148, 176, 176, 149, 149, 150, 150,
+    136, 136, 172, 172, 58, 58, 132, 132, 93, 93, 234, 234, 127, 127, 162, 162,
+    21, 21, 54, 54, 103, 103, 67, 67, 109, 109, 10};
+
+}  // namespace
+
+// A calculator that converts face landmarks to RenderData proto for
+// visualization. Ignores landmark_connections specified in
+// LandmarksToRenderDataCalculatorOptions, if any, and always uses a fixed set
+// of landmark connections specific to face landmark (defined in
+// kFaceLandmarkConnections[] above).
+//
+// Example config:
+// node {
+//   calculator: "FaceLandmarksToRenderDataCalculator"
+//   input_stream: "NORM_LANDMARKS:landmarks"
+//   output_stream: "RENDER_DATA:render_data"
+//   options {
+//     [LandmarksToRenderDataCalculatorOptions.ext] {
+//       landmark_color { r: 0 g: 255 b: 0 }
+//       connection_color { r: 0 g: 255 b: 0 }
+//       thickness: 4.0
+//     }
+//   }
+// }
+class FaceLandmarksToRenderDataCalculator
+    : public LandmarksToRenderDataCalculator {
+ public:
+  absl::Status Open(CalculatorContext* cc) override;
+};
+REGISTER_CALCULATOR(FaceLandmarksToRenderDataCalculator);
+
+absl::Status FaceLandmarksToRenderDataCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  options_ = cc->Options<mediapipe::LandmarksToRenderDataCalculatorOptions>();
+
+  for (int i = 0; i < kNumFaceLandmarkConnections; ++i) {
+    landmark_connections_.push_back(kFaceLandmarkConnections[i * 2]);
+    landmark_connections_.push_back(kFaceLandmarkConnections[i * 2 + 1]);
+  }
+
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/face_mesh/face_mesh_desktop.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_desktop.pbtxt
new file mode 100644
index 0000000..215791a
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/face_mesh_desktop.pbtxt
@@ -0,0 +1,70 @@
+# MediaPipe graph that performs face mesh on desktop with TensorFlow Lite
+# on CPU.
+
+# Path to the input video file. (string)
+input_side_packet: "input_video_path"
+# Path to the output video file. (string)
+input_side_packet: "output_video_path"
+
+# max_queue_size limits the number of packets enqueued on any input stream
+# by throttling inputs to the graph. This makes the graph only process one
+# frame per time.
+max_queue_size: 1
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+# Defines side packets for further use in the graph.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:num_faces"
+  output_side_packet: "PACKET:1:with_attention"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Subgraph that detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontCpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Subgraph that renders face-landmark annotation onto the input video.
+node {
+  calculator: "FaceRendererCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "LANDMARKS:multi_face_landmarks"
+  input_stream: "NORM_RECTS:face_rects_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/face_mesh/face_mesh_desktop_live.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_desktop_live.pbtxt
new file mode 100644
index 0000000..2cc5634
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/face_mesh_desktop_live.pbtxt
@@ -0,0 +1,66 @@
+# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
+
+# Input image. (ImageFrame)
+input_stream: "input_video"
+
+# Output image with rendered results. (ImageFrame)
+output_stream: "output_video"
+# Collection of detected/processed faces, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "multi_face_landmarks"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Defines side packets for further use in the graph.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:num_faces"
+  output_side_packet: "PACKET:1:with_attention"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Subgraph that detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Subgraph that renders face-landmark annotation onto the input image.
+node {
+  calculator: "FaceRendererCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LANDMARKS:multi_face_landmarks"
+  input_stream: "NORM_RECTS:face_rects_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/face_mesh/face_mesh_desktop_live_gpu.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_desktop_live_gpu.pbtxt
new file mode 100644
index 0000000..ae03709
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/face_mesh_desktop_live_gpu.pbtxt
@@ -0,0 +1,66 @@
+# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
+
+# Input image. (GpuBuffer)
+input_stream: "input_video"
+
+# Output image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+# Collection of detected/processed faces, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "multi_face_landmarks"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Defines side packets for further use in the graph.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:num_faces"
+  output_side_packet: "PACKET:1:with_attention"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Subgraph that detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Subgraph that renders face-landmark annotation onto the input image.
+node {
+  calculator: "FaceRendererGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LANDMARKS:multi_face_landmarks"
+  input_stream: "NORM_RECTS:face_rects_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/face_mesh/face_mesh_mobile.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_mobile.pbtxt
new file mode 100644
index 0000000..e9711e1
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/face_mesh_mobile.pbtxt
@@ -0,0 +1,67 @@
+# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# Max number of faces to detect/process. (int)
+input_side_packet: "num_faces"
+
+# Output image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+# Collection of detected/processed faces, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "multi_face_landmarks"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Defines side packets for further use in the graph.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:with_attention"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Subgraph that detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Subgraph that renders face-landmark annotation onto the input image.
+node {
+  calculator: "FaceRendererGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LANDMARKS:multi_face_landmarks"
+  input_stream: "NORM_RECTS:face_rects_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/face_mesh/subgraphs/BUILD b/mediapipe/graphs/face_mesh/subgraphs/BUILD
new file mode 100644
index 0000000..fbb946d
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/subgraphs/BUILD
@@ -0,0 +1,52 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "renderer_calculators",
+    deps = [
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+        "//mediapipe/graphs/face_mesh/calculators:face_landmarks_to_render_data_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_renderer_gpu",
+    graph = "face_renderer_gpu.pbtxt",
+    register_as = "FaceRendererGpu",
+    deps = [
+        ":renderer_calculators",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_renderer_cpu",
+    graph = "face_renderer_cpu.pbtxt",
+    register_as = "FaceRendererCpu",
+    deps = [
+        ":renderer_calculators",
+    ],
+)
diff --git a/mediapipe/graphs/face_mesh/subgraphs/face_renderer_cpu.pbtxt b/mediapipe/graphs/face_mesh/subgraphs/face_renderer_cpu.pbtxt
new file mode 100644
index 0000000..f5793f3
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/subgraphs/face_renderer_cpu.pbtxt
@@ -0,0 +1,96 @@
+# MediaPipe face mesh rendering subgraph.
+
+type: "FaceRendererCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:input_image"
+# Collection of detected/predicted faces, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+input_stream: "LANDMARKS:multi_face_landmarks"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+input_stream: "NORM_RECTS:rects"
+# Detected palms. (std::vector<Detection>)
+input_stream: "DETECTIONS:detections"
+
+# CPU image with rendered data. (ImageFrame)
+output_stream: "IMAGE:output_image"
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:input_image"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:detections_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Outputs each element of multi_face_landmarks at a fake timestamp for the rest
+# of the graph to process. At the end of the loop, outputs the BATCH_END
+# timestamp for downstream calculators to inform them that all elements in the
+# vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITERABLE:multi_face_landmarks"
+  output_stream: "ITEM:face_landmarks"
+  output_stream: "BATCH_END:landmark_timestamp"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "FaceLandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  output_stream: "RENDER_DATA:landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 0 g: 255 b: 0 }
+      thickness: 2
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# Collects a RenderData object for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopRenderDataCalculator"
+  input_stream: "ITEM:landmarks_render_data"
+  input_stream: "BATCH_END:landmark_timestamp"
+  output_stream: "ITERABLE:multi_face_landmarks_render_data"
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECTS:rects"
+  output_stream: "RENDER_DATA:rects_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_image"
+  input_stream: "detections_render_data"
+  input_stream: "VECTOR:0:multi_face_landmarks_render_data"
+  input_stream: "rects_render_data"
+  output_stream: "IMAGE:output_image"
+}
diff --git a/mediapipe/graphs/face_mesh/subgraphs/face_renderer_gpu.pbtxt b/mediapipe/graphs/face_mesh/subgraphs/face_renderer_gpu.pbtxt
new file mode 100644
index 0000000..4e2b3f2
--- /dev/null
+++ b/mediapipe/graphs/face_mesh/subgraphs/face_renderer_gpu.pbtxt
@@ -0,0 +1,96 @@
+# MediaPipe face mesh rendering subgraph.
+
+type: "FaceRendererGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:input_image"
+# Collection of detected/predicted faces, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+input_stream: "LANDMARKS:multi_face_landmarks"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+input_stream: "NORM_RECTS:rects"
+# Detected palms. (std::vector<Detection>)
+input_stream: "DETECTIONS:detections"
+
+# GPU image with rendered data. (GpuBuffer)
+output_stream: "IMAGE:output_image"
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:detections_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Outputs each element of multi_face_landmarks at a fake timestamp for the rest
+# of the graph to process. At the end of the loop, outputs the BATCH_END
+# timestamp for downstream calculators to inform them that all elements in the
+# vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITERABLE:multi_face_landmarks"
+  output_stream: "ITEM:face_landmarks"
+  output_stream: "BATCH_END:end_timestamp"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "FaceLandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  output_stream: "RENDER_DATA:landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 0 g: 255 b: 0 }
+      thickness: 2
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# Collects a RenderData object for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopRenderDataCalculator"
+  input_stream: "ITEM:landmarks_render_data"
+  input_stream: "BATCH_END:end_timestamp"
+  output_stream: "ITERABLE:multi_face_landmarks_render_data"
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECTS:rects"
+  output_stream: "RENDER_DATA:rects_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  input_stream: "detections_render_data"
+  input_stream: "VECTOR:0:multi_face_landmarks_render_data"
+  input_stream: "rects_render_data"
+  output_stream: "IMAGE_GPU:output_image"
+}
diff --git a/mediapipe/graphs/hair_segmentation/BUILD b/mediapipe/graphs/hair_segmentation/BUILD
new file mode 100644
index 0000000..b177726
--- /dev/null
+++ b/mediapipe/graphs/hair_segmentation/BUILD
@@ -0,0 +1,61 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/image:recolor_calculator",
+        "//mediapipe/calculators/image:set_alpha_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_segmentation_calculator",
+        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
+        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
+    ],
+)
+
+cc_library(
+    name = "desktop_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/image:recolor_calculator",
+        "//mediapipe/calculators/image:set_alpha_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_segmentation_calculator",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "mobile_gpu_binary_graph",
+    graph = "hair_segmentation_mobile_gpu.pbtxt",
+    output_name = "mobile_gpu.binarypb",
+    deps = [":mobile_calculators"],
+)
diff --git a/mediapipe/graphs/hair_segmentation/hair_segmentation_desktop_live.pbtxt b/mediapipe/graphs/hair_segmentation/hair_segmentation_desktop_live.pbtxt
new file mode 100644
index 0000000..36c6970
--- /dev/null
+++ b/mediapipe/graphs/hair_segmentation/hair_segmentation_desktop_live.pbtxt
@@ -0,0 +1,152 @@
+# MediaPipe graph that performs hair segmentation with TensorFlow Lite on CPU.
+# Used in the example in
+# mediapipie/examples/desktop/hair_segmentation:hair_segmentation_cpu
+
+# Images on CPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToSegmentationCalculator downstream in the graph to finish
+# generating the corresponding hair mask before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToSegmentationCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:hair_mask"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Transforms the input image on CPU to a 512x512 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the hair
+# segmentation model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "IMAGE:transformed_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 512
+      output_height: 512
+    }
+  }
+}
+
+# Caches a mask fed back from the previous round of hair segmentation, and upon
+# the arrival of the next input image sends out the cached mask with the
+# timestamp replaced by that of the input image, essentially generating a packet
+# that carries the previous mask. Note that upon the arrival of the very first
+# input image, an empty packet is sent out to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:throttled_input_video"
+  input_stream: "LOOP:hair_mask"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:previous_hair_mask"
+}
+
+# Embeds the hair mask generated from the previous round of hair segmentation
+# as the alpha channel of the current input image.
+node {
+  calculator: "SetAlphaCalculator"
+  input_stream: "IMAGE:transformed_input_video"
+  input_stream: "ALPHA:previous_hair_mask"
+  output_stream: "IMAGE:mask_embedded_input_video"
+}
+
+# Converts the transformed input image on CPU into an image tensor stored in
+# TfLiteTensor. The zero_center option is set to false to normalize the
+# pixel values to [0.f, 1.f] as opposed to [-1.f, 1.f]. With the
+# max_num_channels option set to 4, all 4 RGBA channels are contained in the
+# image tensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE:mask_embedded_input_video"
+  output_stream: "TENSORS:image_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
+      zero_center: false
+      max_num_channels: 4
+    }
+  }
+}
+
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "op_resolver"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteCustomOpResolverCalculatorOptions] {
+      use_gpu: false
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# tensor representing the hair segmentation, which has the same width and height
+# as the input image tensor.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:segmentation_tensor"
+  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/hair_segmentation.tflite"
+      use_gpu: false
+    }
+  }
+}
+
+# Decodes the segmentation tensor generated by the TensorFlow Lite model into a
+# mask of values in [0, 255], stored in a CPU buffer. It also
+# takes the mask generated previously as another input to improve the temporal
+# consistency.
+node {
+  calculator: "TfLiteTensorsToSegmentationCalculator"
+  input_stream: "TENSORS:segmentation_tensor"
+  input_stream: "PREV_MASK:previous_hair_mask"
+  output_stream: "MASK:hair_mask"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToSegmentationCalculatorOptions] {
+      tensor_width: 512
+      tensor_height: 512
+      tensor_channels: 2
+      combine_with_previous_ratio: 0.9
+      output_layer_index: 1
+    }
+  }
+}
+
+# Colors the hair segmentation with the color specified in the option.
+node {
+  calculator: "RecolorCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "MASK:hair_mask"
+  output_stream: "IMAGE:output_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
+      color { r: 0 g: 0 b: 255 }
+      mask_channel: RED
+    }
+  }
+}
diff --git a/mediapipe/graphs/hair_segmentation/hair_segmentation_mobile_gpu.pbtxt b/mediapipe/graphs/hair_segmentation/hair_segmentation_mobile_gpu.pbtxt
new file mode 100644
index 0000000..c8db44d
--- /dev/null
+++ b/mediapipe/graphs/hair_segmentation/hair_segmentation_mobile_gpu.pbtxt
@@ -0,0 +1,152 @@
+# MediaPipe graph that performs hair segmentation with TensorFlow Lite on GPU.
+# Used in the example in
+# mediapipie/examples/android/src/java/com/mediapipe/apps/hairsegmentationgpu.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToSegmentationCalculator downstream in the graph to finish
+# generating the corresponding hair mask before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToSegmentationCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:hair_mask"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Transforms the input image on GPU to a 512x512 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the hair
+# segmentation model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  output_stream: "IMAGE_GPU:transformed_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 512
+      output_height: 512
+    }
+  }
+}
+
+# Caches a mask fed back from the previous round of hair segmentation, and upon
+# the arrival of the next input image sends out the cached mask with the
+# timestamp replaced by that of the input image, essentially generating a packet
+# that carries the previous mask. Note that upon the arrival of the very first
+# input image, an empty packet is sent out to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:throttled_input_video"
+  input_stream: "LOOP:hair_mask"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:previous_hair_mask"
+}
+
+# Embeds the hair mask generated from the previous round of hair segmentation
+# as the alpha channel of the current input image.
+node {
+  calculator: "SetAlphaCalculator"
+  input_stream: "IMAGE_GPU:transformed_input_video"
+  input_stream: "ALPHA_GPU:previous_hair_mask"
+  output_stream: "IMAGE_GPU:mask_embedded_input_video"
+}
+
+# Converts the transformed input image on GPU into an image tensor stored in
+# tflite::gpu::GlBuffer. The zero_center option is set to false to normalize the
+# pixel values to [0.f, 1.f] as opposed to [-1.f, 1.f]. With the
+# max_num_channels option set to 4, all 4 RGBA channels are contained in the
+# image tensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE_GPU:mask_embedded_input_video"
+  output_stream: "TENSORS_GPU:image_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
+      zero_center: false
+      max_num_channels: 4
+    }
+  }
+}
+
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "op_resolver"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteCustomOpResolverCalculatorOptions] {
+      use_gpu: true
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# tensor representing the hair segmentation, which has the same width and height
+# as the input image tensor.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS_GPU:image_tensor"
+  output_stream: "TENSORS_GPU:segmentation_tensor"
+  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/hair_segmentation.tflite"
+      use_gpu: true
+    }
+  }
+}
+
+# Decodes the segmentation tensor generated by the TensorFlow Lite model into a
+# mask of values in [0.f, 1.f], stored in the R channel of a GPU buffer. It also
+# takes the mask generated previously as another input to improve the temporal
+# consistency.
+node {
+  calculator: "TfLiteTensorsToSegmentationCalculator"
+  input_stream: "TENSORS_GPU:segmentation_tensor"
+  input_stream: "PREV_MASK_GPU:previous_hair_mask"
+  output_stream: "MASK_GPU:hair_mask"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToSegmentationCalculatorOptions] {
+      tensor_width: 512
+      tensor_height: 512
+      tensor_channels: 2
+      combine_with_previous_ratio: 0.9
+      output_layer_index: 1
+    }
+  }
+}
+
+# Colors the hair segmentation with the color specified in the option.
+node {
+  calculator: "RecolorCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "MASK_GPU:hair_mask"
+  output_stream: "IMAGE_GPU:output_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
+      color { r: 0 g: 0 b: 255 }
+      mask_channel: RED
+    }
+  }
+}
diff --git a/mediapipe/graphs/hand_tracking/BUILD b/mediapipe/graphs/hand_tracking/BUILD
new file mode 100644
index 0000000..71525bb
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/BUILD
@@ -0,0 +1,91 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+exports_files(glob([
+    "*.pbtxt",
+]))
+
+cc_library(
+    name = "desktop_offline_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:immediate_mux_calculator",
+        "//mediapipe/calculators/core:packet_inner_join_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+    ],
+)
+
+cc_library(
+    name = "desktop_tflite_calculators",
+    deps = [
+        ":desktop_offline_calculators",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:merge_calculator",
+        "//mediapipe/graphs/hand_tracking/subgraphs:hand_renderer_cpu",
+        "//mediapipe/modules/hand_landmark:hand_landmark_tracking_cpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "hand_tracking_desktop_live_binary_graph",
+    graph = "hand_tracking_desktop_live.pbtxt",
+    output_name = "hand_tracking_desktop_live.binarypb",
+    deps = [":desktop_tflite_calculators"],
+)
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/graphs/hand_tracking/subgraphs:hand_renderer_gpu",
+        "//mediapipe/modules/hand_landmark:hand_landmark_tracking_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "hand_tracking_mobile_gpu_binary_graph",
+    graph = "hand_tracking_mobile.pbtxt",
+    output_name = "hand_tracking_mobile_gpu.binarypb",
+    deps = [":mobile_calculators"],
+)
+
+cc_library(
+    name = "detection_mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/modules/palm_detection:palm_detection_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "hand_detection_mobile_gpu_binary_graph",
+    graph = "hand_detection_mobile.pbtxt",
+    output_name = "hand_detection_mobile_gpu.binarypb",
+    deps = [":detection_mobile_calculators"],
+)
diff --git a/mediapipe/graphs/hand_tracking/calculators/BUILD b/mediapipe/graphs/hand_tracking/calculators/BUILD
new file mode 100644
index 0000000..3d15861
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/calculators/BUILD
@@ -0,0 +1,17 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
diff --git a/mediapipe/graphs/hand_tracking/hand_detection_desktop.pbtxt b/mediapipe/graphs/hand_tracking/hand_detection_desktop.pbtxt
new file mode 100644
index 0000000..3edcfe7
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/hand_detection_desktop.pbtxt
@@ -0,0 +1,61 @@
+# MediaPipe graph that performs hand detection on desktop with TensorFlow Lite
+# on CPU.
+# Used in the example in
+# mediapipie/examples/desktop/hand_tracking:hand_detection_tflite.
+
+# max_queue_size limits the number of packets enqueued on any input stream
+# by throttling inputs to the graph. This makes the graph only process one
+# frame per time.
+max_queue_size: 1
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+# Detects palms.
+node {
+  calculator: "PalmDetectionCpu"
+  input_stream: "IMAGE:input_video"
+  output_stream: "DETECTIONS:output_detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the original image coming into
+# the graph.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/hand_tracking/hand_detection_desktop_live.pbtxt b/mediapipe/graphs/hand_tracking/hand_detection_desktop_live.pbtxt
new file mode 100644
index 0000000..1bbd8bc
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/hand_detection_desktop_live.pbtxt
@@ -0,0 +1,39 @@
+# MediaPipe graph that performs hand detection on desktop with TensorFlow Lite
+# on CPU.
+# Used in the example in
+# mediapipe/examples/desktop/hand_tracking:hand_detection_cpu.
+
+# CPU image. (ImageFrame)
+input_stream: "input_video"
+
+# CPU image. (ImageFrame)
+output_stream: "output_video"
+
+# Detects palms.
+node {
+  calculator: "PalmDetectionCpu"
+  input_stream: "IMAGE:input_video"
+  output_stream: "DETECTIONS:output_detections"
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the original image coming into
+# the graph.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt b/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt
new file mode 100644
index 0000000..74ff5c5
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt
@@ -0,0 +1,59 @@
+# MediaPipe graph that performs hand detection with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/handdetectiongpu and
+# mediapipe/examples/ios/handdetectiongpu.
+
+# GPU image. (GpuBuffer)
+input_stream: "input_video"
+
+# GPU image. (GpuBuffer)
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for PalmDetectionGpu
+# downstream in the graph to finish its tasks before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images in PalmDetectionGpu to 1. This prevents the nodes in
+# PalmDetectionGpu from queuing up incoming images and data excessively, which
+# leads to increased latency and memory usage, unwanted in real-time mobile
+# applications. It also eliminates unnecessarily computation, e.g., the output
+# produced by a node in the subgraph may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Detects palms.
+node {
+  calculator: "PalmDetectionGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:palm_detections"
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:palm_detections"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "detection_render_data"
+  output_stream: "IMAGE_GPU:output_video"
+}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt
new file mode 100644
index 0000000..bc6e81c
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt
@@ -0,0 +1,68 @@
+# MediaPipe graph that performs hands tracking on desktop with TensorFlow Lite
+# on CPU.
+# Used in the example in
+# mediapipe/examples/desktop/hand_tracking:hand_tracking_tflite.
+
+# max_queue_size limits the number of packets enqueued on any input stream
+# by throttling inputs to the graph. This makes the graph only process one
+# frame per time.
+max_queue_size: 1
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+# Generates side packet cotaining max number of hands to detect/track.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:num_hands"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 2 }
+    }
+  }
+}
+
+# Detects/tracks hand landmarks.
+node {
+  calculator: "HandLandmarkTrackingCpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_HANDS:num_hands"
+  output_stream: "LANDMARKS:landmarks"
+  output_stream: "HANDEDNESS:handedness"
+  output_stream: "PALM_DETECTIONS:multi_palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:multi_hand_rects"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:multi_palm_rects"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see hand_renderer_cpu.pbtxt).
+node {
+  calculator: "HandRendererSubgraph"
+  input_stream: "IMAGE:input_video"
+  input_stream: "DETECTIONS:multi_palm_detections"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "HANDEDNESS:handedness"
+  input_stream: "NORM_RECTS:0:multi_palm_rects"
+  input_stream: "NORM_RECTS:1:multi_hand_rects"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
new file mode 100644
index 0000000..20de18f
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
@@ -0,0 +1,46 @@
+# MediaPipe graph that performs hands tracking on desktop with TensorFlow
+# Lite on CPU.
+# Used in the example in
+# mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu.
+
+# CPU image. (ImageFrame)
+input_stream: "input_video"
+
+# CPU image. (ImageFrame)
+output_stream: "output_video"
+
+# Generates side packet cotaining max number of hands to detect/track.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:num_hands"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 2 }
+    }
+  }
+}
+
+# Detects/tracks hand landmarks.
+node {
+  calculator: "HandLandmarkTrackingCpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_HANDS:num_hands"
+  output_stream: "LANDMARKS:landmarks"
+  output_stream: "HANDEDNESS:handedness"
+  output_stream: "PALM_DETECTIONS:multi_palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:multi_hand_rects"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:multi_palm_rects"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see hand_renderer_cpu.pbtxt).
+node {
+  calculator: "HandRendererSubgraph"
+  input_stream: "IMAGE:input_video"
+  input_stream: "DETECTIONS:multi_palm_detections"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "HANDEDNESS:handedness"
+  input_stream: "NORM_RECTS:0:multi_palm_rects"
+  input_stream: "NORM_RECTS:1:multi_hand_rects"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt
new file mode 100644
index 0000000..4dcaac5
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt
@@ -0,0 +1,48 @@
+# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/handtrackinggpu.
+
+# GPU image. (GpuBuffer)
+input_stream: "input_video"
+
+# GPU image. (GpuBuffer)
+output_stream: "output_video"
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "hand_landmarks"
+
+# Generates side packet cotaining max number of hands to detect/track.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:num_hands"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 2 }
+    }
+  }
+}
+
+# Detects/tracks hand landmarks.
+node {
+  calculator: "HandLandmarkTrackingGpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_HANDS:num_hands"
+  output_stream: "LANDMARKS:hand_landmarks"
+  output_stream: "HANDEDNESS:handedness"
+  output_stream: "PALM_DETECTIONS:palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects_from_landmarks"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see hand_renderer_gpu.pbtxt).
+node {
+  calculator: "HandRendererSubgraph"
+  input_stream: "IMAGE:input_video"
+  input_stream: "DETECTIONS:palm_detections"
+  input_stream: "LANDMARKS:hand_landmarks"
+  input_stream: "HANDEDNESS:handedness"
+  input_stream: "NORM_RECTS:0:hand_rects_from_palm_detections"
+  input_stream: "NORM_RECTS:1:hand_rects_from_landmarks"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
new file mode 100644
index 0000000..b47e2a4
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
@@ -0,0 +1,65 @@
+# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/handtrackinggpu.
+
+# GPU image. (GpuBuffer)
+input_stream: "input_video"
+
+# Max number of hands to detect/process. (int)
+input_side_packet: "num_hands"
+
+# Model complexity (0 or 1). (int)
+input_side_packet: "model_complexity"
+
+# GPU image. (GpuBuffer)
+output_stream: "output_video"
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "hand_landmarks"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Detects/tracks hand landmarks.
+node {
+  calculator: "HandLandmarkTrackingGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_side_packet: "NUM_HANDS:num_hands"
+  output_stream: "LANDMARKS:hand_landmarks"
+  output_stream: "HANDEDNESS:handedness"
+  output_stream: "PALM_DETECTIONS:palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects_from_landmarks"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see hand_renderer_gpu.pbtxt).
+node {
+  calculator: "HandRendererSubgraph"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "DETECTIONS:palm_detections"
+  input_stream: "LANDMARKS:hand_landmarks"
+  input_stream: "HANDEDNESS:handedness"
+  input_stream: "NORM_RECTS:0:hand_rects_from_palm_detections"
+  input_stream: "NORM_RECTS:1:hand_rects_from_landmarks"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/hand_tracking/subgraphs/BUILD b/mediapipe/graphs/hand_tracking/subgraphs/BUILD
new file mode 100644
index 0000000..f16a6db
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/subgraphs/BUILD
@@ -0,0 +1,58 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "hand_renderer_cpu",
+    graph = "hand_renderer_cpu.pbtxt",
+    register_as = "HandRendererSubgraph",
+    deps = [
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:labels_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_renderer_gpu",
+    graph = "hand_renderer_gpu.pbtxt",
+    register_as = "HandRendererSubgraph",
+    deps = [
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:labels_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+    ],
+)
diff --git a/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_cpu.pbtxt b/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_cpu.pbtxt
new file mode 100644
index 0000000..eed1388
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_cpu.pbtxt
@@ -0,0 +1,209 @@
+# MediaPipe graph to render hand landmarks and some related debug information.
+
+type: "HandRendererSubgraph"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:input_image"
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+input_stream: "LANDMARKS:multi_hand_landmarks"
+# Handedness of the detected hand (i.e. is hand left or right).
+# (std::vector<ClassificationList>)
+input_stream: "HANDEDNESS:multi_handedness"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+input_stream: "NORM_RECTS:0:multi_palm_rects"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+input_stream: "NORM_RECTS:1:multi_hand_rects"
+# Detected palms. (std::vector<Detection>)
+input_stream: "DETECTIONS:palm_detections"
+
+# Updated CPU image. (ImageFrame)
+output_stream: "IMAGE:output_image"
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:palm_detections"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECTS:multi_hand_rects"
+  output_stream: "RENDER_DATA:multi_hand_rects_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECTS:multi_palm_rects"
+  output_stream: "RENDER_DATA:multi_palm_rects_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 125 g: 0 b: 122 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Outputs each element of multi_palm_landmarks at a fake timestamp for the rest
+# of the graph to process. At the end of the loop, outputs the BATCH_END
+# timestamp for downstream calculators to inform them that all elements in the
+# vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITERABLE:multi_hand_landmarks"
+  output_stream: "ITEM:single_hand_landmarks"
+  output_stream: "BATCH_END:landmark_timestamp"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:single_hand_landmarks"
+  output_stream: "RENDER_DATA:single_hand_landmark_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 0
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 5
+      landmark_connections: 9
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 9
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 14
+      landmark_connections: 15
+      landmark_connections: 15
+      landmark_connections: 16
+      landmark_connections: 13
+      landmark_connections: 17
+      landmark_connections: 0
+      landmark_connections: 17
+      landmark_connections: 17
+      landmark_connections: 18
+      landmark_connections: 18
+      landmark_connections: 19
+      landmark_connections: 19
+      landmark_connections: 20
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 0 g: 255 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Collects a RenderData object for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopRenderDataCalculator"
+  input_stream: "ITEM:single_hand_landmark_render_data"
+  input_stream: "BATCH_END:landmark_timestamp"
+  output_stream: "ITERABLE:multi_hand_landmarks_render_data"
+}
+
+# Don't render handedness if there are more than one handedness reported.
+node {
+  calculator: "ClassificationListVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:multi_handedness"
+  output_stream: "disallow_handedness_rendering"
+  node_options: {
+    [type.googleapis.com/mediapipe.CollectionHasMinSizeCalculatorOptions] {
+      min_size: 2
+    }
+  }
+}
+
+node {
+  calculator: "GateCalculator"
+  input_stream: "multi_handedness"
+  input_stream: "DISALLOW:disallow_handedness_rendering"
+  output_stream: "allowed_multi_handedness"
+  node_options: {
+    [type.googleapis.com/mediapipe.GateCalculatorOptions] {
+      empty_packets_as_allow: false
+    }
+  }
+}
+
+node {
+  calculator: "SplitClassificationListVectorCalculator"
+  input_stream: "allowed_multi_handedness"
+  output_stream: "handedness"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Converts classification to drawing primitives for annotation overlay.
+node {
+  calculator: "LabelsToRenderDataCalculator"
+  input_stream: "CLASSIFICATIONS:handedness"
+  output_stream: "RENDER_DATA:handedness_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LabelsToRenderDataCalculatorOptions]: {
+      color { r: 255 g: 0 b: 0 }
+      thickness: 10.0
+      font_height_px: 50
+      horizontal_offset_px: 30
+      vertical_offset_px: 50
+
+      max_num_labels: 1
+      location: TOP_LEFT
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images. Consumes
+# a vector of RenderData objects and draws each of them on the input frame.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_image"
+  input_stream: "detection_render_data"
+  input_stream: "multi_hand_rects_render_data"
+  input_stream: "multi_palm_rects_render_data"
+  input_stream: "handedness_render_data"
+  input_stream: "VECTOR:0:multi_hand_landmarks_render_data"
+  output_stream: "IMAGE:output_image"
+}
diff --git a/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_gpu.pbtxt b/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_gpu.pbtxt
new file mode 100644
index 0000000..9f0af85
--- /dev/null
+++ b/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_gpu.pbtxt
@@ -0,0 +1,209 @@
+# MediaPipe graph to render hand landmarks and some related debug information.
+
+type: "HandRendererSubgraph"
+
+# GPU buffer. (GpuBuffer)
+input_stream: "IMAGE:input_image"
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+input_stream: "LANDMARKS:multi_hand_landmarks"
+# Handedness of the detected hand (i.e. is hand left or right).
+# (std::vector<ClassificationList>)
+input_stream: "HANDEDNESS:multi_handedness"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+input_stream: "NORM_RECTS:0:multi_palm_rects"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+input_stream: "NORM_RECTS:1:multi_hand_rects"
+# Detected palms. (std::vector<Detection>)
+input_stream: "DETECTIONS:palm_detections"
+
+# Updated GPU buffer. (GpuBuffer)
+output_stream: "IMAGE:output_image"
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:palm_detections"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECTS:multi_hand_rects"
+  output_stream: "RENDER_DATA:multi_hand_rects_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECTS:multi_palm_rects"
+  output_stream: "RENDER_DATA:multi_palm_rects_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 125 g: 0 b: 122 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Outputs each element of multi_palm_landmarks at a fake timestamp for the rest
+# of the graph to process. At the end of the loop, outputs the BATCH_END
+# timestamp for downstream calculators to inform them that all elements in the
+# vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITERABLE:multi_hand_landmarks"
+  output_stream: "ITEM:single_hand_landmarks"
+  output_stream: "BATCH_END:landmark_timestamp"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:single_hand_landmarks"
+  output_stream: "RENDER_DATA:single_hand_landmark_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 0
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 5
+      landmark_connections: 9
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 9
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 14
+      landmark_connections: 15
+      landmark_connections: 15
+      landmark_connections: 16
+      landmark_connections: 13
+      landmark_connections: 17
+      landmark_connections: 0
+      landmark_connections: 17
+      landmark_connections: 17
+      landmark_connections: 18
+      landmark_connections: 18
+      landmark_connections: 19
+      landmark_connections: 19
+      landmark_connections: 20
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 0 g: 255 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Collects a RenderData object for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopRenderDataCalculator"
+  input_stream: "ITEM:single_hand_landmark_render_data"
+  input_stream: "BATCH_END:landmark_timestamp"
+  output_stream: "ITERABLE:multi_hand_landmarks_render_data"
+}
+
+# Don't render handedness if there are more than one handedness reported.
+node {
+  calculator: "ClassificationListVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:multi_handedness"
+  output_stream: "disallow_handedness_rendering"
+  node_options: {
+    [type.googleapis.com/mediapipe.CollectionHasMinSizeCalculatorOptions] {
+      min_size: 2
+    }
+  }
+}
+
+node {
+  calculator: "GateCalculator"
+  input_stream: "multi_handedness"
+  input_stream: "DISALLOW:disallow_handedness_rendering"
+  output_stream: "allowed_multi_handedness"
+  node_options: {
+    [type.googleapis.com/mediapipe.GateCalculatorOptions] {
+      empty_packets_as_allow: false
+    }
+  }
+}
+
+node {
+  calculator: "SplitClassificationListVectorCalculator"
+  input_stream: "allowed_multi_handedness"
+  output_stream: "handedness"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Converts classification to drawing primitives for annotation overlay.
+node {
+  calculator: "LabelsToRenderDataCalculator"
+  input_stream: "CLASSIFICATIONS:handedness"
+  output_stream: "RENDER_DATA:handedness_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LabelsToRenderDataCalculatorOptions]: {
+      color { r: 255 g: 0 b: 0 }
+      thickness: 10.0
+      font_height_px: 50
+      horizontal_offset_px: 30
+      vertical_offset_px: 50
+
+      max_num_labels: 1
+      location: TOP_LEFT
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images. Consumes
+# a vector of RenderData objects and draws each of them on the input frame.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  input_stream: "detection_render_data"
+  input_stream: "multi_hand_rects_render_data"
+  input_stream: "multi_palm_rects_render_data"
+  input_stream: "handedness_render_data"
+  input_stream: "VECTOR:0:multi_hand_landmarks_render_data"
+  output_stream: "IMAGE_GPU:output_image"
+}
diff --git a/mediapipe/graphs/holistic_tracking/BUILD b/mediapipe/graphs/holistic_tracking/BUILD
new file mode 100644
index 0000000..986cf9f
--- /dev/null
+++ b/mediapipe/graphs/holistic_tracking/BUILD
@@ -0,0 +1,70 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+    "mediapipe_simple_subgraph",
+)
+
+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])
+
+mediapipe_simple_subgraph(
+    name = "holistic_tracking_to_render_data",
+    graph = "holistic_tracking_to_render_data.pbtxt",
+    register_as = "HolisticTrackingToRenderData",
+    deps = [
+        "//mediapipe/calculators/core:concatenate_normalized_landmark_list_calculator",
+        "//mediapipe/calculators/core:concatenate_vector_calculator",
+        "//mediapipe/calculators/core:merge_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_scale_calculator",
+        "//mediapipe/modules/holistic_landmark:hand_wrist_for_pose",
+    ],
+)
+
+cc_library(
+    name = "holistic_tracking_gpu_deps",
+    deps = [
+        ":holistic_tracking_to_render_data",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/modules/holistic_landmark:holistic_landmark_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "holistic_tracking_gpu",
+    graph = "holistic_tracking_gpu.pbtxt",
+    output_name = "holistic_tracking_gpu.binarypb",
+    deps = [":holistic_tracking_gpu_deps"],
+)
+
+cc_library(
+    name = "holistic_tracking_cpu_graph_deps",
+    deps = [
+        ":holistic_tracking_to_render_data",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/modules/holistic_landmark:holistic_landmark_cpu",
+    ],
+)
diff --git a/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt b/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt
new file mode 100644
index 0000000..fead245
--- /dev/null
+++ b/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt
@@ -0,0 +1,75 @@
+# Tracks and renders pose + hands + face landmarks.
+
+# CPU image. (ImageFrame)
+input_stream: "input_video"
+
+# CPU image with rendered results. (ImageFrame)
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.FlowLimiterCalculatorOptions] {
+      max_in_flight: 1
+      max_in_queue: 1
+      # Timeout is disabled (set to 0) as first frame processing can take more
+      # than 1 second.
+      in_flight_timeout: 0
+    }
+  }
+}
+
+node {
+  calculator: "HolisticLandmarkCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "POSE_LANDMARKS:pose_landmarks"
+  output_stream: "POSE_ROI:pose_roi"
+  output_stream: "POSE_DETECTION:pose_detection"
+  output_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+}
+
+# Gets image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts pose, hands and face landmarks to a render data vector.
+node {
+  calculator: "HolisticTrackingToRenderData"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "POSE_LANDMARKS:pose_landmarks"
+  input_stream: "POSE_ROI:pose_roi"
+  input_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+  input_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+  input_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "RENDER_DATA_VECTOR:render_data_vector"
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "VECTOR:render_data_vector"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt b/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt
new file mode 100644
index 0000000..dc85be4
--- /dev/null
+++ b/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt
@@ -0,0 +1,75 @@
+# Tracks and renders pose + hands + face landmarks.
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# GPU image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.FlowLimiterCalculatorOptions] {
+      max_in_flight: 1
+      max_in_queue: 1
+      # Timeout is disabled (set to 0) as first frame processing can take more
+      # than 1 second.
+      in_flight_timeout: 0
+    }
+  }
+}
+
+node {
+  calculator: "HolisticLandmarkGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "POSE_LANDMARKS:pose_landmarks"
+  output_stream: "POSE_ROI:pose_roi"
+  output_stream: "POSE_DETECTION:pose_detection"
+  output_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+}
+
+# Gets image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts pose, hands and face landmarks to a render data vector.
+node {
+  calculator: "HolisticTrackingToRenderData"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "POSE_LANDMARKS:pose_landmarks"
+  input_stream: "POSE_ROI:pose_roi"
+  input_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+  input_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+  input_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "RENDER_DATA_VECTOR:render_data_vector"
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "VECTOR:render_data_vector"
+  output_stream: "IMAGE_GPU:output_video"
+}
diff --git a/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt b/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt
new file mode 100644
index 0000000..4b05123
--- /dev/null
+++ b/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt
@@ -0,0 +1,757 @@
+# Converts pose + hands + face landmarks to a render data vector.
+
+type: "HolisticTrackingToRenderData"
+
+# Image size. (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+# Pose landmarks. (NormalizedLandmarkList)
+input_stream: "POSE_LANDMARKS:landmarks"
+# Region of interest calculated based on pose landmarks. (NormalizedRect)
+input_stream: "POSE_ROI:roi"
+# Left hand landmarks. (NormalizedLandmarkList)
+input_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+# Right hand landmarks. (NormalizedLandmarkList)
+input_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+# Face landmarks. (NormalizedLandmarkList)
+input_stream: "FACE_LANDMARKS:face_landmarks"
+
+# Render data vector. (std::vector<RenderData>)
+output_stream: "RENDER_DATA_VECTOR:render_data_vector"
+
+# --------------------------------------------------------------------------- #
+# ------------------ Calculates scale for render objects -------------------- #
+# --------------------------------------------------------------------------- #
+
+# Calculates rendering scale based on the pose bounding box.
+node {
+  calculator: "RectToRenderScaleCalculator"
+  input_stream: "NORM_RECT:roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "RENDER_SCALE:render_scale"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderScaleCalculatorOptions] {
+      multiplier: 0.0008
+    }
+  }
+}
+
+# --------------------------------------------------------------------------- #
+# --------------- Combines pose and hands into pose skeleton ---------------- #
+# --------------------------------------------------------------------------- #
+
+# Gets pose landmarks before wrists.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks"
+  output_stream: "landmarks_before_wrist"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 11 end: 15 }
+    }
+  }
+}
+
+# Gets pose left wrist landmark.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks"
+  output_stream: "landmarks_left_wrist"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 15 end: 16 }
+    }
+  }
+}
+
+# Gets pose right wrist landmark.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks"
+  output_stream: "landmarks_right_wrist"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 16 end: 17 }
+    }
+  }
+}
+
+# Gets pose landmarks after wrists.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks"
+  output_stream: "landmarks_after_wrist"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 23 end: 33 }
+    }
+  }
+}
+
+# Gets left hand wrist landmark.
+node {
+  calculator: "HandWristForPose"
+  input_stream: "HAND_LANDMARKS:left_hand_landmarks"
+  output_stream: "WRIST_LANDMARK:left_hand_wrist_landmark"
+}
+
+# Gets left hand wrist landmark or keep pose wrist landmark if hand was not
+# predicted.
+node {
+  calculator: "MergeCalculator"
+  input_stream: "left_hand_wrist_landmark"
+  input_stream: "landmarks_left_wrist"
+  output_stream: "merged_left_hand_wrist_landmark"
+}
+
+# Gets right hand wrist landmark.
+node {
+  calculator: "HandWristForPose"
+  input_stream: "HAND_LANDMARKS:right_hand_landmarks"
+  output_stream: "WRIST_LANDMARK:right_hand_wrist_landmark"
+}
+
+# Gets right hand wrist landmark or keep pose wrist landmark if hand was not
+# predicted.
+node {
+  calculator: "MergeCalculator"
+  input_stream: "right_hand_wrist_landmark"
+  input_stream: "landmarks_right_wrist"
+  output_stream: "merged_right_hand_wrist_landmark"
+}
+
+# Combines pose landmarks all together.
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "landmarks_before_wrist"
+  input_stream: "merged_left_hand_wrist_landmark"
+  input_stream: "merged_right_hand_wrist_landmark"
+  input_stream: "landmarks_after_wrist"
+  output_stream: "landmarks_merged"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConcatenateVectorCalculatorOptions] {
+      only_emit_if_all_present: true
+    }
+  }
+}
+
+# Takes left pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks_merged"
+  output_stream: "landmarks_left_side"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 2 end: 3 }
+      ranges: { begin: 4 end: 5 }
+      ranges: { begin: 6 end: 7 }
+      ranges: { begin: 8 end: 9 }
+      ranges: { begin: 10 end: 11 }
+      ranges: { begin: 12 end: 13 }
+      ranges: { begin: 14 end: 15 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Takes right pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks_merged"
+  output_stream: "landmarks_right_side"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 1 end: 2 }
+      ranges: { begin: 3 end: 4 }
+      ranges: { begin: 5 end: 6 }
+      ranges: { begin: 7 end: 8 }
+      ranges: { begin: 9 end: 10 }
+      ranges: { begin: 11 end: 12 }
+      ranges: { begin: 13 end: 14 }
+      ranges: { begin: 15 end: 16 }
+      combine_outputs: true
+    }
+  }
+}
+
+# --------------------------------------------------------------------------- #
+# ---------------------------------- Pose ----------------------------------- #
+# --------------------------------------------------------------------------- #
+
+# Converts pose connections to white lines.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_merged"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 0
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 4
+      landmark_connections: 1
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 5
+      landmark_connections: 0
+      landmark_connections: 6
+      landmark_connections: 1
+      landmark_connections: 7
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 6
+      landmark_connections: 8
+      landmark_connections: 7
+      landmark_connections: 9
+      landmark_connections: 8
+      landmark_connections: 10
+      landmark_connections: 9
+      landmark_connections: 11
+      landmark_connections: 10
+      landmark_connections: 12
+      landmark_connections: 11
+      landmark_connections: 13
+      landmark_connections: 12
+      landmark_connections: 14
+      landmark_connections: 13
+      landmark_connections: 15
+      landmark_connections: 10
+      landmark_connections: 14
+      landmark_connections: 11
+      landmark_connections: 15
+
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.1
+    }
+  }
+}
+
+# Converts pose joints to big white circles.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_merged"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_background_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 5.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Converts pose left side joints to orange circles (inside white ones).
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_left_side"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_left_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 138 b: 0 }
+      connection_color { r: 255 g: 138 b: 0 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Converts pose right side joints to cyan circles (inside white ones).
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_right_side"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_right_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 0 g: 217 b: 231 }
+      connection_color { r: 0 g: 217 b: 231 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# --------------------------------------------------------------------------- #
+# ------------------------------- Left hand --------------------------------- #
+# --------------------------------------------------------------------------- #
+
+# Converts left hand connections to white lines.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:left_hand_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:left_hand_landmarks_connections_rd"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 0
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 5
+      landmark_connections: 9
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 9
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 14
+      landmark_connections: 15
+      landmark_connections: 15
+      landmark_connections: 16
+      landmark_connections: 13
+      landmark_connections: 17
+      landmark_connections: 0
+      landmark_connections: 17
+      landmark_connections: 17
+      landmark_connections: 18
+      landmark_connections: 18
+      landmark_connections: 19
+      landmark_connections: 19
+      landmark_connections: 20
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 4.0
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# Converts left hand color joints.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:left_hand_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:left_hand_landmarks_joints_rd"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 138 b: 0 }
+      connection_color { r: 255 g: 138 b: 0 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# --------------------------------------------------------------------------- #
+# -------------------------------- Right hand ------------------------------- #
+# --------------------------------------------------------------------------- #
+
+# Converts right hand connections to white lines.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:right_hand_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:right_hand_landmarks_connections_rd"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 0
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 5
+      landmark_connections: 9
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 9
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 14
+      landmark_connections: 15
+      landmark_connections: 15
+      landmark_connections: 16
+      landmark_connections: 13
+      landmark_connections: 17
+      landmark_connections: 0
+      landmark_connections: 17
+      landmark_connections: 17
+      landmark_connections: 18
+      landmark_connections: 18
+      landmark_connections: 19
+      landmark_connections: 19
+      landmark_connections: 20
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 4.0
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# Converts right hand color joints.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:right_hand_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:right_hand_landmarks_joints_rd"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 0 g: 217 b: 231 }
+      connection_color { r: 0 g: 217 b: 231 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# --------------------------------------------------------------------------- #
+# ---------------------------------- Face ----------------------------------- #
+# --------------------------------------------------------------------------- #
+
+# Converts face connections to white lines.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:face_landmarks_connections_rd"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      # Lips.
+      landmark_connections: 61
+      landmark_connections: 146
+      landmark_connections: 146
+      landmark_connections: 91
+      landmark_connections: 91
+      landmark_connections: 181
+      landmark_connections: 181
+      landmark_connections: 84
+      landmark_connections: 84
+      landmark_connections: 17
+      landmark_connections: 17
+      landmark_connections: 314
+      landmark_connections: 314
+      landmark_connections: 405
+      landmark_connections: 405
+      landmark_connections: 321
+      landmark_connections: 321
+      landmark_connections: 375
+      landmark_connections: 375
+      landmark_connections: 291
+      landmark_connections: 61
+      landmark_connections: 185
+      landmark_connections: 185
+      landmark_connections: 40
+      landmark_connections: 40
+      landmark_connections: 39
+      landmark_connections: 39
+      landmark_connections: 37
+      landmark_connections: 37
+      landmark_connections: 0
+      landmark_connections: 0
+      landmark_connections: 267
+      landmark_connections: 267
+      landmark_connections: 269
+      landmark_connections: 269
+      landmark_connections: 270
+      landmark_connections: 270
+      landmark_connections: 409
+      landmark_connections: 409
+      landmark_connections: 291
+      landmark_connections: 78
+      landmark_connections: 95
+      landmark_connections: 95
+      landmark_connections: 88
+      landmark_connections: 88
+      landmark_connections: 178
+      landmark_connections: 178
+      landmark_connections: 87
+      landmark_connections: 87
+      landmark_connections: 14
+      landmark_connections: 14
+      landmark_connections: 317
+      landmark_connections: 317
+      landmark_connections: 402
+      landmark_connections: 402
+      landmark_connections: 318
+      landmark_connections: 318
+      landmark_connections: 324
+      landmark_connections: 324
+      landmark_connections: 308
+      landmark_connections: 78
+      landmark_connections: 191
+      landmark_connections: 191
+      landmark_connections: 80
+      landmark_connections: 80
+      landmark_connections: 81
+      landmark_connections: 81
+      landmark_connections: 82
+      landmark_connections: 82
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 312
+      landmark_connections: 312
+      landmark_connections: 311
+      landmark_connections: 311
+      landmark_connections: 310
+      landmark_connections: 310
+      landmark_connections: 415
+      landmark_connections: 415
+      landmark_connections: 308
+      # Left eye.
+      landmark_connections: 33
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 163
+      landmark_connections: 163
+      landmark_connections: 144
+      landmark_connections: 144
+      landmark_connections: 145
+      landmark_connections: 145
+      landmark_connections: 153
+      landmark_connections: 153
+      landmark_connections: 154
+      landmark_connections: 154
+      landmark_connections: 155
+      landmark_connections: 155
+      landmark_connections: 133
+      landmark_connections: 33
+      landmark_connections: 246
+      landmark_connections: 246
+      landmark_connections: 161
+      landmark_connections: 161
+      landmark_connections: 160
+      landmark_connections: 160
+      landmark_connections: 159
+      landmark_connections: 159
+      landmark_connections: 158
+      landmark_connections: 158
+      landmark_connections: 157
+      landmark_connections: 157
+      landmark_connections: 173
+      landmark_connections: 173
+      landmark_connections: 133
+      # Left eyebrow.
+      landmark_connections: 46
+      landmark_connections: 53
+      landmark_connections: 53
+      landmark_connections: 52
+      landmark_connections: 52
+      landmark_connections: 65
+      landmark_connections: 65
+      landmark_connections: 55
+      landmark_connections: 70
+      landmark_connections: 63
+      landmark_connections: 63
+      landmark_connections: 105
+      landmark_connections: 105
+      landmark_connections: 66
+      landmark_connections: 66
+      landmark_connections: 107
+      # Right eye.
+      landmark_connections: 263
+      landmark_connections: 249
+      landmark_connections: 249
+      landmark_connections: 390
+      landmark_connections: 390
+      landmark_connections: 373
+      landmark_connections: 373
+      landmark_connections: 374
+      landmark_connections: 374
+      landmark_connections: 380
+      landmark_connections: 380
+      landmark_connections: 381
+      landmark_connections: 381
+      landmark_connections: 382
+      landmark_connections: 382
+      landmark_connections: 362
+      landmark_connections: 263
+      landmark_connections: 466
+      landmark_connections: 466
+      landmark_connections: 388
+      landmark_connections: 388
+      landmark_connections: 387
+      landmark_connections: 387
+      landmark_connections: 386
+      landmark_connections: 386
+      landmark_connections: 385
+      landmark_connections: 385
+      landmark_connections: 384
+      landmark_connections: 384
+      landmark_connections: 398
+      landmark_connections: 398
+      landmark_connections: 362
+      # Right eyebrow.
+      landmark_connections: 276
+      landmark_connections: 283
+      landmark_connections: 283
+      landmark_connections: 282
+      landmark_connections: 282
+      landmark_connections: 295
+      landmark_connections: 295
+      landmark_connections: 285
+      landmark_connections: 300
+      landmark_connections: 293
+      landmark_connections: 293
+      landmark_connections: 334
+      landmark_connections: 334
+      landmark_connections: 296
+      landmark_connections: 296
+      landmark_connections: 336
+      # Face oval.
+      landmark_connections: 10
+      landmark_connections: 338
+      landmark_connections: 338
+      landmark_connections: 297
+      landmark_connections: 297
+      landmark_connections: 332
+      landmark_connections: 332
+      landmark_connections: 284
+      landmark_connections: 284
+      landmark_connections: 251
+      landmark_connections: 251
+      landmark_connections: 389
+      landmark_connections: 389
+      landmark_connections: 356
+      landmark_connections: 356
+      landmark_connections: 454
+      landmark_connections: 454
+      landmark_connections: 323
+      landmark_connections: 323
+      landmark_connections: 361
+      landmark_connections: 361
+      landmark_connections: 288
+      landmark_connections: 288
+      landmark_connections: 397
+      landmark_connections: 397
+      landmark_connections: 365
+      landmark_connections: 365
+      landmark_connections: 379
+      landmark_connections: 379
+      landmark_connections: 378
+      landmark_connections: 378
+      landmark_connections: 400
+      landmark_connections: 400
+      landmark_connections: 377
+      landmark_connections: 377
+      landmark_connections: 152
+      landmark_connections: 152
+      landmark_connections: 148
+      landmark_connections: 148
+      landmark_connections: 176
+      landmark_connections: 176
+      landmark_connections: 149
+      landmark_connections: 149
+      landmark_connections: 150
+      landmark_connections: 150
+      landmark_connections: 136
+      landmark_connections: 136
+      landmark_connections: 172
+      landmark_connections: 172
+      landmark_connections: 58
+      landmark_connections: 58
+      landmark_connections: 132
+      landmark_connections: 132
+      landmark_connections: 93
+      landmark_connections: 93
+      landmark_connections: 234
+      landmark_connections: 234
+      landmark_connections: 127
+      landmark_connections: 127
+      landmark_connections: 162
+      landmark_connections: 162
+      landmark_connections: 21
+      landmark_connections: 21
+      landmark_connections: 54
+      landmark_connections: 54
+      landmark_connections: 103
+      landmark_connections: 103
+      landmark_connections: 67
+      landmark_connections: 67
+      landmark_connections: 109
+      landmark_connections: 109
+      landmark_connections: 10
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 0.5
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# Converts face joints to cyan circles.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:face_landmarks_joints_rd"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 0 g: 217 b: 231 }
+      connection_color { r: 0 g: 217 b: 231 }
+      thickness: 0.5
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+# Concatenates all render data.
+node {
+  calculator: "ConcatenateRenderDataVectorCalculator"
+  input_stream: "landmarks_render_data"
+  input_stream: "landmarks_background_joints_render_data"
+  input_stream: "landmarks_left_joints_render_data"
+  input_stream: "landmarks_right_joints_render_data"
+
+  # Left hand.
+  input_stream: "left_hand_landmarks_connections_rd"
+  input_stream: "left_hand_landmarks_joints_rd"
+
+  # Right hand.
+  input_stream: "right_hand_landmarks_connections_rd"
+  input_stream: "right_hand_landmarks_joints_rd"
+
+  # Face.
+  input_stream: "face_landmarks_connections_rd"
+  input_stream: "face_landmarks_joints_rd"
+
+  output_stream: "render_data_vector"
+}
diff --git a/mediapipe/graphs/instant_motion_tracking/BUILD b/mediapipe/graphs/instant_motion_tracking/BUILD
new file mode 100644
index 0000000..e9be587
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/BUILD
@@ -0,0 +1,39 @@
+# Copyright 2020 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "instant_motion_tracking_deps",
+    deps = [
+        "//mediapipe/graphs/instant_motion_tracking/calculators:matrices_manager_calculator",
+        "//mediapipe/graphs/instant_motion_tracking/calculators:sticker_manager_calculator",
+        "//mediapipe/graphs/instant_motion_tracking/subgraphs:region_tracking",
+        "//mediapipe/graphs/object_detection_3d/calculators:gl_animation_overlay_calculator",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "instant_motion_tracking_binary_graph",
+    graph = "instant_motion_tracking.pbtxt",
+    output_name = "instant_motion_tracking.binarypb",
+    deps = [":instant_motion_tracking_deps"],
+)
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/BUILD b/mediapipe/graphs/instant_motion_tracking/calculators/BUILD
new file mode 100644
index 0000000..93af68c
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/calculators/BUILD
@@ -0,0 +1,84 @@
+# Copyright 2020 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+proto_library(
+    name = "sticker_buffer_proto",
+    srcs = [
+        "sticker_buffer.proto",
+    ],
+)
+
+mediapipe_cc_proto_library(
+    name = "sticker_buffer_cc_proto",
+    srcs = [
+        "sticker_buffer.proto",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":sticker_buffer_proto",
+    ],
+)
+
+cc_library(
+    name = "sticker_manager_calculator",
+    srcs = ["sticker_manager_calculator.cc"],
+    hdrs = ["transformations.h"],
+    deps = [
+        ":sticker_buffer_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:timestamp",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "matrices_manager_calculator",
+    srcs = ["matrices_manager_calculator.cc"],
+    hdrs = ["transformations.h"],
+    deps = [
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:timestamp",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/graphs/object_detection_3d/calculators:model_matrix_cc_proto",
+        "//mediapipe/modules/objectron/calculators:box",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings",
+        "@eigen_archive//:eigen3",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "tracked_anchor_manager_calculator",
+    srcs = ["tracked_anchor_manager_calculator.cc"],
+    hdrs = ["transformations.h"],
+    deps = [
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/util/tracking:box_tracker_cc_proto",
+    ],
+    alwayslink = 1,
+)
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/matrices_manager_calculator.cc b/mediapipe/graphs/instant_motion_tracking/calculators/matrices_manager_calculator.cc
new file mode 100644
index 0000000..c003135
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/calculators/matrices_manager_calculator.cc
@@ -0,0 +1,393 @@
+// Copyright 2020 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cmath>
+#include <memory>
+
+#include "Eigen/Core"
+#include "Eigen/Dense"
+#include "Eigen/Geometry"
+#include "absl/memory/memory.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/str_join.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/graphs/instant_motion_tracking/calculators/transformations.h"
+#include "mediapipe/graphs/object_detection_3d/calculators/model_matrix.pb.h"
+#include "mediapipe/modules/objectron/calculators/box.h"
+
+namespace mediapipe {
+
+namespace {
+using Matrix4fCM = Eigen::Matrix<float, 4, 4, Eigen::ColMajor>;
+using Vector3f = Eigen::Vector3f;
+using Matrix3f = Eigen::Matrix3f;
+using DiagonalMatrix3f = Eigen::DiagonalMatrix<float, 3>;
+constexpr char kAnchorsTag[] = "ANCHORS";
+constexpr char kIMUMatrixTag[] = "IMU_ROTATION";
+constexpr char kUserRotationsTag[] = "USER_ROTATIONS";
+constexpr char kUserScalingsTag[] = "USER_SCALINGS";
+constexpr char kRendersTag[] = "RENDER_DATA";
+constexpr char kGifAspectRatioTag[] = "GIF_ASPECT_RATIO";
+constexpr char kFOVSidePacketTag[] = "FOV";
+constexpr char kAspectRatioSidePacketTag[] = "ASPECT_RATIO";
+// initial Z value (-10 is center point in visual range for OpenGL render)
+constexpr float kInitialZ = -10.0f;
+}  // namespace
+
+// Intermediary for rotation and translation data to model matrix usable by
+// gl_animation_overlay_calculator. For information on the construction of
+// OpenGL objects and transformations (including a breakdown of model matrices),
+// please visit: https://open.gl/transformations
+//
+// Input Side Packets:
+//  FOV - Vertical field of view for device [REQUIRED - Defines perspective
+//  matrix] ASPECT_RATIO - Aspect ratio of device [REQUIRED - Defines
+//  perspective matrix]
+//
+// Input streams:
+//  ANCHORS - Anchor data with x,y,z coordinates (x,y are in [0.0-1.0] range for
+//    position on the device screen, while z is the scaling factor that changes
+//    in proportion to the distance from the tracked region) [REQUIRED]
+//  IMU_ROTATION - float[9] of row-major device rotation matrix [REQUIRED]
+//  USER_ROTATIONS - UserRotations with corresponding radians of rotation
+//    [REQUIRED]
+//  USER_SCALINGS - UserScalings with corresponding scale factor [REQUIRED]
+//  GIF_ASPECT_RATIO - Aspect ratio of GIF image used to dynamically scale
+//    GIF asset defined as width / height [OPTIONAL]
+// Output:
+//  MATRICES - TimedModelMatrixProtoList of each object type to render
+//    [REQUIRED]
+//
+// Example config:
+// node{
+//  calculator: "MatricesManagerCalculator"
+//  input_stream: "ANCHORS:tracked_scaled_anchor_data"
+//  input_stream: "IMU_ROTATION:imu_rotation_matrix"
+//  input_stream: "USER_ROTATIONS:user_rotation_data"
+//  input_stream: "USER_SCALINGS:user_scaling_data"
+//  input_stream: "GIF_ASPECT_RATIO:gif_aspect_ratio"
+//  output_stream: "MATRICES:0:first_render_matrices"
+//  output_stream: "MATRICES:1:second_render_matrices" [unbounded input size]
+//  input_side_packet: "FOV:vertical_fov_radians"
+//  input_side_packet: "ASPECT_RATIO:aspect_ratio"
+// }
+
+class MatricesManagerCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  // Device properties that will be preset by side packets
+  float vertical_fov_radians_ = 0.0f;
+  float aspect_ratio_ = 0.0f;
+  float gif_aspect_ratio_ = 1.0f;
+
+  const Matrix3f GenerateUserRotationMatrix(const float rotation_radians) const;
+  const Matrix4fCM GenerateEigenModelMatrix(
+      const Vector3f& translation_vector,
+      const Matrix3f& rotation_submatrix) const;
+  const Vector3f GenerateAnchorVector(const Anchor& tracked_anchor) const;
+  DiagonalMatrix3f GetDefaultRenderScaleDiagonal(
+      const int render_id, const float user_scale_factor,
+      const float gif_aspect_ratio) const;
+
+  // Returns a user scaling increment associated with the sticker_id
+  // TODO: Adjust lookup function if total number of stickers is uncapped to
+  // improve performance
+  const float GetUserScaler(const std::vector<UserScaling>& scalings,
+                            const int sticker_id) const {
+    for (const UserScaling& user_scaling : scalings) {
+      if (user_scaling.sticker_id == sticker_id) {
+        return user_scaling.scale_factor;
+      }
+    }
+    LOG(WARNING) << "Cannot find sticker_id: " << sticker_id
+                 << ", returning 1.0f scaling";
+    return 1.0f;
+  }
+
+  // Returns a user rotation in radians associated with the sticker_id
+  const float GetUserRotation(const std::vector<UserRotation>& rotations,
+                              const int sticker_id) {
+    for (const UserRotation& rotation : rotations) {
+      if (rotation.sticker_id == sticker_id) {
+        return rotation.rotation_radians;
+      }
+    }
+    LOG(WARNING) << "Cannot find sticker_id: " << sticker_id
+                 << ", returning 0.0f rotation";
+    return 0.0f;
+  }
+};
+
+REGISTER_CALCULATOR(MatricesManagerCalculator);
+
+absl::Status MatricesManagerCalculator::GetContract(CalculatorContract* cc) {
+  RET_CHECK(cc->Inputs().HasTag(kAnchorsTag) &&
+            cc->Inputs().HasTag(kIMUMatrixTag) &&
+            cc->Inputs().HasTag(kUserRotationsTag) &&
+            cc->Inputs().HasTag(kUserScalingsTag) &&
+            cc->InputSidePackets().HasTag(kFOVSidePacketTag) &&
+            cc->InputSidePackets().HasTag(kAspectRatioSidePacketTag));
+
+  cc->Inputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
+  cc->Inputs().Tag(kIMUMatrixTag).Set<float[]>();
+  cc->Inputs().Tag(kUserScalingsTag).Set<std::vector<UserScaling>>();
+  cc->Inputs().Tag(kUserRotationsTag).Set<std::vector<UserRotation>>();
+  cc->Inputs().Tag(kRendersTag).Set<std::vector<int>>();
+  if (cc->Inputs().HasTag(kGifAspectRatioTag)) {
+    cc->Inputs().Tag(kGifAspectRatioTag).Set<float>();
+  }
+
+  for (CollectionItemId id = cc->Outputs().BeginId("MATRICES");
+       id < cc->Outputs().EndId("MATRICES"); ++id) {
+    cc->Outputs().Get(id).Set<mediapipe::TimedModelMatrixProtoList>();
+  }
+  cc->InputSidePackets().Tag(kFOVSidePacketTag).Set<float>();
+  cc->InputSidePackets().Tag(kAspectRatioSidePacketTag).Set<float>();
+
+  return absl::OkStatus();
+}
+
+absl::Status MatricesManagerCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  // Set device properties from side packets
+  vertical_fov_radians_ =
+      cc->InputSidePackets().Tag(kFOVSidePacketTag).Get<float>();
+  aspect_ratio_ =
+      cc->InputSidePackets().Tag(kAspectRatioSidePacketTag).Get<float>();
+  return absl::OkStatus();
+}
+
+absl::Status MatricesManagerCalculator::Process(CalculatorContext* cc) {
+  // Define each object's model matrices
+  auto asset_matrices_gif =
+      std::make_unique<mediapipe::TimedModelMatrixProtoList>();
+  auto asset_matrices_1 =
+      std::make_unique<mediapipe::TimedModelMatrixProtoList>();
+  // Clear all model matrices
+  asset_matrices_gif->clear_model_matrix();
+  asset_matrices_1->clear_model_matrix();
+
+  const std::vector<UserRotation> user_rotation_data =
+      cc->Inputs().Tag(kUserRotationsTag).Get<std::vector<UserRotation>>();
+
+  const std::vector<UserScaling> user_scaling_data =
+      cc->Inputs().Tag(kUserScalingsTag).Get<std::vector<UserScaling>>();
+
+  const std::vector<int> render_data =
+      cc->Inputs().Tag(kRendersTag).Get<std::vector<int>>();
+
+  const std::vector<Anchor> anchor_data =
+      cc->Inputs().Tag(kAnchorsTag).Get<std::vector<Anchor>>();
+  if (cc->Inputs().HasTag(kGifAspectRatioTag) &&
+      !cc->Inputs().Tag(kGifAspectRatioTag).IsEmpty()) {
+    gif_aspect_ratio_ = cc->Inputs().Tag(kGifAspectRatioTag).Get<float>();
+  }
+
+  // Device IMU rotation submatrix
+  const auto imu_matrix = cc->Inputs().Tag(kIMUMatrixTag).Get<float[]>();
+  Matrix3f imu_rotation_submatrix;
+  int idx = 0;
+  for (int x = 0; x < 3; ++x) {
+    for (int y = 0; y < 3; ++y) {
+      // Input matrix is row-major matrix, it must be reformatted to
+      // column-major via transpose procedure
+      imu_rotation_submatrix(y, x) = imu_matrix[idx++];
+    }
+  }
+
+  int render_idx = 0;
+  for (const Anchor& anchor : anchor_data) {
+    const int id = anchor.sticker_id;
+    mediapipe::TimedModelMatrixProto* model_matrix;
+    // Add model matrix to matrices list for defined object render ID
+    if (render_data[render_idx] == 0) {  // GIF
+      model_matrix = asset_matrices_gif->add_model_matrix();
+    } else {  // Asset 3D
+      if (render_data[render_idx] != 1) {
+        LOG(ERROR) << "render id: " << render_data[render_idx]
+                   << " is not supported. Fall back to using render_id = 1.";
+      }
+      model_matrix = asset_matrices_1->add_model_matrix();
+    }
+
+    model_matrix->set_id(id);
+
+    // The user transformation data associated with this sticker must be defined
+    const float user_rotation_radians = GetUserRotation(user_rotation_data, id);
+    const float user_scale_factor = GetUserScaler(user_scaling_data, id);
+
+    // A vector representative of a user's sticker rotation transformation can
+    // be created
+    const Matrix3f user_rotation_submatrix =
+        GenerateUserRotationMatrix(user_rotation_radians);
+    // Next, the diagonal representative of the combined scaling data
+    const DiagonalMatrix3f scaling_diagonal = GetDefaultRenderScaleDiagonal(
+        render_data[render_idx], user_scale_factor, gif_aspect_ratio_);
+    // Increment to next render id from vector
+    render_idx++;
+
+    // The user transformation data can be concatenated into a final rotation
+    // submatrix with the device IMU rotational data
+    const Matrix3f user_transformed_rotation_submatrix =
+        imu_rotation_submatrix * user_rotation_submatrix * scaling_diagonal;
+
+    // A vector representative of the translation of the object in OpenGL
+    // coordinate space must be generated
+    const Vector3f translation_vector = GenerateAnchorVector(anchor);
+
+    // Concatenate all model matrix data
+    const Matrix4fCM final_model_matrix = GenerateEigenModelMatrix(
+        translation_vector, user_transformed_rotation_submatrix);
+
+    // The generated model matrix must be mapped to TimedModelMatrixProto
+    // (col-wise)
+    for (int x = 0; x < final_model_matrix.rows(); ++x) {
+      for (int y = 0; y < final_model_matrix.cols(); ++y) {
+        model_matrix->add_matrix_entries(final_model_matrix(x, y));
+      }
+    }
+  }
+
+  // Output all individual render matrices
+  // TODO: Perform depth ordering with gl_animation_overlay_calculator to render
+  // objects in order by depth to allow occlusion.
+  cc->Outputs()
+      .Get(cc->Outputs().GetId("MATRICES", 0))
+      .Add(asset_matrices_gif.release(), cc->InputTimestamp());
+  cc->Outputs()
+      .Get(cc->Outputs().GetId("MATRICES", 1))
+      .Add(asset_matrices_1.release(), cc->InputTimestamp());
+
+  return absl::OkStatus();
+}
+
+// Using a specified rotation value in radians, generate a rotation matrix for
+// use with base rotation submatrix
+const Matrix3f MatricesManagerCalculator::GenerateUserRotationMatrix(
+    const float rotation_radians) const {
+  Eigen::Matrix3f user_rotation_submatrix;
+  user_rotation_submatrix =
+      // The rotation in radians must be inverted to rotate the object
+      // with the direction of finger movement from the user (system dependent)
+      Eigen::AngleAxisf(-rotation_radians, Eigen::Vector3f::UnitY()) *
+      Eigen::AngleAxisf(0.0f, Eigen::Vector3f::UnitZ()) *
+      // Model orientations all assume z-axis is up, but we need y-axis upwards,
+      // therefore, a +(M_PI * 0.5f) transformation must be applied
+      // TODO: Bring default rotations, translations, and scalings into
+      // independent sticker configuration
+      Eigen::AngleAxisf(M_PI * 0.5f, Eigen::Vector3f::UnitX());
+  // Matrix must be transposed due to the method of submatrix generation in
+  // Eigen
+  return user_rotation_submatrix.transpose();
+}
+
+// TODO: Investigate possible differences in warping of tracking speed across
+// screen Using the sticker anchor data, a translation vector can be generated
+// in OpenGL coordinate space
+const Vector3f MatricesManagerCalculator::GenerateAnchorVector(
+    const Anchor& tracked_anchor) const {
+  // Using an initial z-value in OpenGL space, generate a new base z-axis value
+  // to mimic scaling by distance.
+  const float z = kInitialZ * tracked_anchor.z;
+
+  // Using triangle geometry, the minimum for a y-coordinate that will appear in
+  // the view field for the given z value above can be found.
+  const float y_half_range = z * (tan(vertical_fov_radians_ * 0.5f));
+
+  // The aspect ratio of the device and y_minimum calculated above can be used
+  // to find the minimum value for x that will appear in the view field of the
+  // device screen.
+  const float x_half_range = y_half_range * aspect_ratio_;
+
+  // Given the minimum bounds of the screen in OpenGL space, the tracked anchor
+  // coordinates can be converted to OpenGL coordinate space.
+  //
+  // (i.e: X and Y will be converted from [0.0-1.0] space to [x_minimum,
+  // -x_minimum] space and [y_minimum, -y_minimum] space respectively)
+  const float x = (-2.0f * tracked_anchor.x * x_half_range) + x_half_range;
+  const float y = (-2.0f * tracked_anchor.y * y_half_range) + y_half_range;
+
+  // A translation transformation vector can be generated via Eigen
+  const Vector3f t_vector(x, y, z);
+  return t_vector;
+}
+
+// Generates a model matrix via Eigen with appropriate transformations
+const Matrix4fCM MatricesManagerCalculator::GenerateEigenModelMatrix(
+    const Vector3f& translation_vector,
+    const Matrix3f& rotation_submatrix) const {
+  // Define basic empty model matrix
+  Matrix4fCM mvp_matrix;
+
+  // Set the translation vector
+  mvp_matrix.topRightCorner<3, 1>() = translation_vector;
+
+  // Set the rotation submatrix
+  mvp_matrix.topLeftCorner<3, 3>() = rotation_submatrix;
+
+  // Set trailing 1.0 required by OpenGL to define coordinate space
+  mvp_matrix(3, 3) = 1.0f;
+
+  return mvp_matrix;
+}
+
+// This returns a scaling matrix to alter the projection matrix for
+// the specified render id in order to ensure all objects render at a similar
+// size in the view screen upon initial placement
+DiagonalMatrix3f MatricesManagerCalculator::GetDefaultRenderScaleDiagonal(
+    const int render_id, const float user_scale_factor,
+    const float gif_aspect_ratio) const {
+  float scale_preset = 1.0f;
+  float x_scalar = 1.0f;
+  float y_scalar = 1.0f;
+
+  switch (render_id) {
+    case 0: {  // GIF
+      // 160 is the scaling preset to make the GIF asset appear relatively
+      // similar in size to all other assets
+      scale_preset = 160.0f;
+      if (gif_aspect_ratio >= 1.0f) {
+        // GIF is wider horizontally (scale on x-axis)
+        x_scalar = gif_aspect_ratio;
+      } else {
+        // GIF is wider vertically (scale on y-axis)
+        y_scalar = 1.0f / gif_aspect_ratio;
+      }
+      break;
+    }
+    case 1: {  // 3D asset
+      // 5 is the scaling preset to make the 3D asset appear relatively
+      // similar in size to all other assets
+      scale_preset = 5.0f;
+      break;
+    }
+    default: {
+      LOG(INFO) << "Unsupported render_id: " << render_id
+                << ", returning default render_scale";
+      break;
+    }
+  }
+
+  DiagonalMatrix3f scaling(scale_preset * user_scale_factor * x_scalar,
+                           scale_preset * user_scale_factor * y_scalar,
+                           scale_preset * user_scale_factor);
+  return scaling;
+}
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.proto b/mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.proto
new file mode 100644
index 0000000..b73209c
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.proto
@@ -0,0 +1,33 @@
+// Copyright 2020 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+option java_package = "com.google.mediapipe.graphs.instantmotiontracking";
+option java_outer_classname = "StickerBufferProto";
+
+message Sticker {
+  optional int32 id = 1;
+  optional float x = 2;
+  optional float y = 3;
+  optional float rotation = 4;
+  optional float scale = 5;
+  optional int32 render_id = 6;
+}
+
+message StickerRoll {
+  repeated Sticker sticker = 1;
+}
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/sticker_manager_calculator.cc b/mediapipe/graphs/instant_motion_tracking/calculators/sticker_manager_calculator.cc
new file mode 100644
index 0000000..40210c2
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/calculators/sticker_manager_calculator.cc
@@ -0,0 +1,150 @@
+// Copyright 2020 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <vector>
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.pb.h"
+#include "mediapipe/graphs/instant_motion_tracking/calculators/transformations.h"
+
+namespace mediapipe {
+
+constexpr char kProtoDataString[] = "PROTO";
+constexpr char kAnchorsTag[] = "ANCHORS";
+constexpr char kUserRotationsTag[] = "USER_ROTATIONS";
+constexpr char kUserScalingsTag[] = "USER_SCALINGS";
+constexpr char kRenderDescriptorsTag[] = "RENDER_DATA";
+
+// This calculator takes in the sticker protobuffer data and parses each
+// individual sticker object into anchors, user rotations and scalings, in
+// addition to basic render data represented in integer form.
+//
+// Input:
+//  PROTO - String of sticker data in appropriate protobuf format [REQUIRED]
+// Output:
+//  ANCHORS - Anchors with initial normalized X,Y coordinates [REQUIRED]
+//  USER_ROTATIONS - UserRotations with radians of rotation from user [REQUIRED]
+//  USER_SCALINGS - UserScalings with increment of scaling from user [REQUIRED]
+//  RENDER_DATA - Descriptors of which objects/animations to render for stickers
+//  [REQUIRED]
+//
+// Example config:
+// node {
+//   calculator: "StickerManagerCalculator"
+//   input_stream: "PROTO:sticker_proto_string"
+//   output_stream: "ANCHORS:initial_anchor_data"
+//   output_stream: "USER_ROTATIONS:user_rotation_data"
+//   output_stream: "USER_SCALINGS:user_scaling_data"
+//   output_stream: "RENDER_DATA:sticker_render_data"
+// }
+
+class StickerManagerCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    RET_CHECK(cc->Inputs().HasTag(kProtoDataString));
+    RET_CHECK(cc->Outputs().HasTag(kAnchorsTag) &&
+              cc->Outputs().HasTag(kUserRotationsTag) &&
+              cc->Outputs().HasTag(kUserScalingsTag) &&
+              cc->Outputs().HasTag(kRenderDescriptorsTag));
+
+    cc->Inputs().Tag(kProtoDataString).Set<std::string>();
+    cc->Outputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
+    cc->Outputs().Tag(kUserRotationsTag).Set<std::vector<UserRotation>>();
+    cc->Outputs().Tag(kUserScalingsTag).Set<std::vector<UserScaling>>();
+    cc->Outputs().Tag(kRenderDescriptorsTag).Set<std::vector<int>>();
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Open(CalculatorContext* cc) override {
+    cc->SetOffset(TimestampDiff(0));
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    std::string sticker_proto_string =
+        cc->Inputs().Tag(kProtoDataString).Get<std::string>();
+
+    std::vector<Anchor> initial_anchor_data;
+    std::vector<UserRotation> user_rotation_data;
+    std::vector<UserScaling> user_scaling_data;
+    std::vector<int> render_data;
+
+    ::mediapipe::StickerRoll sticker_roll;
+    bool parse_success = sticker_roll.ParseFromString(sticker_proto_string);
+
+    // Ensure parsing was a success
+    RET_CHECK(parse_success) << "Error parsing sticker protobuf data";
+
+    for (int i = 0; i < sticker_roll.sticker().size(); ++i) {
+      // Declare empty structures for sticker data
+      Anchor initial_anchor;
+      UserRotation user_rotation;
+      UserScaling user_scaling;
+      // Get individual Sticker object as defined by Protobuffer
+      ::mediapipe::Sticker sticker = sticker_roll.sticker(i);
+      // Set individual data structure ids to associate with this sticker
+      initial_anchor.sticker_id = sticker.id();
+      user_rotation.sticker_id = sticker.id();
+      user_scaling.sticker_id = sticker.id();
+      initial_anchor.x = sticker.x();
+      initial_anchor.y = sticker.y();
+      initial_anchor.z = 1.0f;  // default to 1.0 in normalized 3d space
+      user_rotation.rotation_radians = sticker.rotation();
+      user_scaling.scale_factor = sticker.scale();
+      const int render_id = sticker.render_id();
+      // Set all vector data with sticker attributes
+      initial_anchor_data.emplace_back(initial_anchor);
+      user_rotation_data.emplace_back(user_rotation);
+      user_scaling_data.emplace_back(user_scaling);
+      render_data.emplace_back(render_id);
+    }
+
+    if (cc->Outputs().HasTag(kAnchorsTag)) {
+      cc->Outputs()
+          .Tag(kAnchorsTag)
+          .AddPacket(MakePacket<std::vector<Anchor>>(initial_anchor_data)
+                         .At(cc->InputTimestamp()));
+    }
+    if (cc->Outputs().HasTag(kUserRotationsTag)) {
+      cc->Outputs()
+          .Tag(kUserRotationsTag)
+          .AddPacket(MakePacket<std::vector<UserRotation>>(user_rotation_data)
+                         .At(cc->InputTimestamp()));
+    }
+    if (cc->Outputs().HasTag(kUserScalingsTag)) {
+      cc->Outputs()
+          .Tag(kUserScalingsTag)
+          .AddPacket(MakePacket<std::vector<UserScaling>>(user_scaling_data)
+                         .At(cc->InputTimestamp()));
+    }
+    if (cc->Outputs().HasTag(kRenderDescriptorsTag)) {
+      cc->Outputs()
+          .Tag(kRenderDescriptorsTag)
+          .AddPacket(MakePacket<std::vector<int>>(render_data)
+                         .At(cc->InputTimestamp()));
+    }
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Close(CalculatorContext* cc) override {
+    return absl::OkStatus();
+  }
+};
+
+REGISTER_CALCULATOR(StickerManagerCalculator);
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/tracked_anchor_manager_calculator.cc b/mediapipe/graphs/instant_motion_tracking/calculators/tracked_anchor_manager_calculator.cc
new file mode 100644
index 0000000..446aee7
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/calculators/tracked_anchor_manager_calculator.cc
@@ -0,0 +1,210 @@
+// Copyright 2020 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/graphs/instant_motion_tracking/calculators/transformations.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace mediapipe {
+
+constexpr char kSentinelTag[] = "SENTINEL";
+constexpr char kAnchorsTag[] = "ANCHORS";
+constexpr char kBoxesInputTag[] = "BOXES";
+constexpr char kBoxesOutputTag[] = "START_POS";
+constexpr char kCancelTag[] = "CANCEL_ID";
+// TODO: Find optimal Height/Width (0.1-0.3)
+constexpr float kBoxEdgeSize =
+    0.2f;  // Used to establish tracking box dimensions
+constexpr float kUsToMs =
+    1000.0f;  // Used to convert from microseconds to millis
+
+// This calculator manages the regions being tracked for each individual sticker
+// and adjusts the regions being tracked if a change is detected in a sticker's
+// initial anchor placement. Regions being tracked that have no associated
+// sticker will be automatically removed upon the next iteration of the graph to
+// optimize performance and remove all sticker artifacts
+//
+// Input:
+//  SENTINEL - ID of sticker which has an anchor that must be reset (-1 when no
+//  anchor must be reset) [REQUIRED]
+//  ANCHORS - Initial anchor data (tracks changes and where to re/position)
+//  [REQUIRED] BOXES - Used in cycle, boxes being tracked meant to update
+//  positions [OPTIONAL
+//  - provided by subgraph]
+// Output:
+//  START_POS - Positions of boxes being tracked (can be overwritten with ID)
+//  [REQUIRED] CANCEL_ID - Single integer ID of tracking box to remove from
+//  tracker subgraph [OPTIONAL] ANCHORS - Updated set of anchors with tracked
+//  and normalized X,Y,Z [REQUIRED]
+//
+// Example config:
+// node {
+//   calculator: "TrackedAnchorManagerCalculator"
+//   input_stream: "SENTINEL:sticker_sentinel"
+//   input_stream: "ANCHORS:initial_anchor_data"
+//   input_stream: "BOXES:boxes"
+//   input_stream_info: {
+//     tag_index: 'BOXES'
+//     back_edge: true
+//   }
+//   output_stream: "START_POS:start_pos"
+//   output_stream: "CANCEL_ID:cancel_object_id"
+//   output_stream: "ANCHORS:tracked_scaled_anchor_data"
+// }
+
+class TrackedAnchorManagerCalculator : public CalculatorBase {
+ private:
+  // Previous graph iteration anchor data
+  std::vector<Anchor> previous_anchor_data_;
+
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    RET_CHECK(cc->Inputs().HasTag(kAnchorsTag) &&
+              cc->Inputs().HasTag(kSentinelTag));
+    RET_CHECK(cc->Outputs().HasTag(kAnchorsTag) &&
+              cc->Outputs().HasTag(kBoxesOutputTag));
+
+    cc->Inputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
+    cc->Inputs().Tag(kSentinelTag).Set<int>();
+
+    if (cc->Inputs().HasTag(kBoxesInputTag)) {
+      cc->Inputs().Tag(kBoxesInputTag).Set<mediapipe::TimedBoxProtoList>();
+    }
+
+    cc->Outputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
+    cc->Outputs().Tag(kBoxesOutputTag).Set<mediapipe::TimedBoxProtoList>();
+
+    if (cc->Outputs().HasTag(kCancelTag)) {
+      cc->Outputs().Tag(kCancelTag).Set<int>();
+    }
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Open(CalculatorContext* cc) override { return absl::OkStatus(); }
+
+  absl::Status Process(CalculatorContext* cc) override;
+};
+REGISTER_CALCULATOR(TrackedAnchorManagerCalculator);
+
+absl::Status TrackedAnchorManagerCalculator::Process(CalculatorContext* cc) {
+  mediapipe::Timestamp timestamp = cc->InputTimestamp();
+  const int sticker_sentinel = cc->Inputs().Tag(kSentinelTag).Get<int>();
+  std::vector<Anchor> current_anchor_data =
+      cc->Inputs().Tag(kAnchorsTag).Get<std::vector<Anchor>>();
+  auto pos_boxes = absl::make_unique<mediapipe::TimedBoxProtoList>();
+  std::vector<Anchor> tracked_scaled_anchor_data;
+
+  // Delete any boxes being tracked without an associated anchor
+  for (const mediapipe::TimedBoxProto& box :
+       cc->Inputs()
+           .Tag(kBoxesInputTag)
+           .Get<mediapipe::TimedBoxProtoList>()
+           .box()) {
+    bool anchor_exists = false;
+    for (Anchor anchor : current_anchor_data) {
+      if (box.id() == anchor.sticker_id) {
+        anchor_exists = true;
+        break;
+      }
+    }
+    if (!anchor_exists) {
+      cc->Outputs()
+          .Tag(kCancelTag)
+          .AddPacket(MakePacket<int>(box.id()).At(timestamp++));
+    }
+  }
+
+  // Perform tracking or updating for each anchor position
+  for (const Anchor& anchor : current_anchor_data) {
+    Anchor output_anchor = anchor;
+    // Check if anchor position is being reset by user in this graph iteration
+    if (sticker_sentinel == anchor.sticker_id) {
+      // Delete associated tracking box
+      // TODO: BoxTrackingSubgraph should accept vector to avoid breaking
+      // timestamp rules
+      cc->Outputs()
+          .Tag(kCancelTag)
+          .AddPacket(MakePacket<int>(anchor.sticker_id).At(timestamp++));
+      // Add a tracking box
+      mediapipe::TimedBoxProto* box = pos_boxes->add_box();
+      box->set_left(anchor.x - kBoxEdgeSize * 0.5f);
+      box->set_right(anchor.x + kBoxEdgeSize * 0.5f);
+      box->set_top(anchor.y - kBoxEdgeSize * 0.5f);
+      box->set_bottom(anchor.y + kBoxEdgeSize * 0.5f);
+      box->set_id(anchor.sticker_id);
+      box->set_time_msec((timestamp++).Microseconds() / kUsToMs);
+      // Default value for normalized z (scale factor)
+      output_anchor.z = 1.0f;
+    } else {
+      // Anchor position was not reset by user
+      // Attempt to update anchor position from tracking subgraph
+      // (TimedBoxProto)
+      bool updated_from_tracker = false;
+      const mediapipe::TimedBoxProtoList box_list =
+          cc->Inputs().Tag(kBoxesInputTag).Get<mediapipe::TimedBoxProtoList>();
+      for (const auto& box : box_list.box()) {
+        if (box.id() == anchor.sticker_id) {
+          // Get center x normalized coordinate [0.0-1.0]
+          output_anchor.x = (box.left() + box.right()) * 0.5f;
+          // Get center y normalized coordinate [0.0-1.0]
+          output_anchor.y = (box.top() + box.bottom()) * 0.5f;
+          // Get center z coordinate [z starts at normalized 1.0 and scales
+          // inversely with box-width]
+          // TODO: Look into issues with uniform scaling on x-axis and y-axis
+          output_anchor.z = kBoxEdgeSize / (box.right() - box.left());
+          updated_from_tracker = true;
+          break;
+        }
+      }
+      // If anchor position was not updated from tracker, create new tracking
+      // box at last recorded anchor coordinates. This will allow all current
+      // stickers to be tracked at approximately last location even if
+      // re-acquisitioning in the BoxTrackingSubgraph encounters errors
+      if (!updated_from_tracker) {
+        for (const Anchor& prev_anchor : previous_anchor_data_) {
+          if (anchor.sticker_id == prev_anchor.sticker_id) {
+            mediapipe::TimedBoxProto* box = pos_boxes->add_box();
+            box->set_left(prev_anchor.x - kBoxEdgeSize * 0.5f);
+            box->set_right(prev_anchor.x + kBoxEdgeSize * 0.5f);
+            box->set_top(prev_anchor.y - kBoxEdgeSize * 0.5f);
+            box->set_bottom(prev_anchor.y + kBoxEdgeSize * 0.5f);
+            box->set_id(prev_anchor.sticker_id);
+            box->set_time_msec(cc->InputTimestamp().Microseconds() / kUsToMs);
+            output_anchor = prev_anchor;
+            // Default value for normalized z (scale factor)
+            output_anchor.z = 1.0f;
+            break;
+          }
+        }
+      }
+    }
+    tracked_scaled_anchor_data.emplace_back(output_anchor);
+  }
+  // Set anchor data for next iteration
+  previous_anchor_data_ = tracked_scaled_anchor_data;
+
+  cc->Outputs()
+      .Tag(kAnchorsTag)
+      .AddPacket(MakePacket<std::vector<Anchor>>(tracked_scaled_anchor_data)
+                     .At(cc->InputTimestamp()));
+  cc->Outputs()
+      .Tag(kBoxesOutputTag)
+      .Add(pos_boxes.release(), cc->InputTimestamp());
+
+  return absl::OkStatus();
+}
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/transformations.h b/mediapipe/graphs/instant_motion_tracking/calculators/transformations.h
new file mode 100644
index 0000000..cbacdb7
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/calculators/transformations.h
@@ -0,0 +1,42 @@
+// Copyright 2020 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_GRAPHS_INSTANT_MOTION_TRACKING_CALCULATORS_TRANSFORMATIONS_H_
+#define MEDIAPIPE_GRAPHS_INSTANT_MOTION_TRACKING_CALCULATORS_TRANSFORMATIONS_H_
+
+namespace mediapipe {
+
+// Radians by which to rotate the object (Provided by UI input)
+struct UserRotation {
+  float rotation_radians;
+  int sticker_id;
+};
+
+// Scaling factor provided by the UI application end
+struct UserScaling {
+  float scale_factor;
+  int sticker_id;
+};
+
+// The normalized anchor coordinates of a sticker
+struct Anchor {
+  float x;  // [0.0-1.0]
+  float y;  // [0.0-1.0]
+  float z;  // Centered around 1.0 [current_scale = z * initial_scale]
+  int sticker_id;
+};
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_GRAPHS_INSTANT_MOTION_TRACKING_CALCULATORS_TRANSFORMATIONS_H_
diff --git a/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt b/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt
new file mode 100644
index 0000000..468262b
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt
@@ -0,0 +1,80 @@
+# Copyright 2020 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# MediaPipe graph that performs region tracking and 3d object (AR sticker) rendering.
+
+# Images in/out of graph with sticker data and IMU information from device
+input_stream: "input_video"
+input_stream: "sticker_sentinel"
+input_stream: "sticker_proto_string"
+input_stream: "imu_rotation_matrix"
+input_stream: "gif_texture"
+input_stream: "gif_aspect_ratio"
+output_stream: "output_video"
+
+# Converts sticker data into user data (rotations/scalings), render data, and
+# initial anchors.
+node {
+  calculator: "StickerManagerCalculator"
+  input_stream: "PROTO:sticker_proto_string"
+  output_stream: "ANCHORS:initial_anchor_data"
+  output_stream: "USER_ROTATIONS:user_rotation_data"
+  output_stream: "USER_SCALINGS:user_scaling_data"
+  output_stream: "RENDER_DATA:sticker_render_data"
+}
+
+# Uses box tracking in order to create 'anchors' for associated 3d stickers.
+node {
+  calculator: "RegionTrackingSubgraph"
+  input_stream: "VIDEO:input_video"
+  input_stream: "SENTINEL:sticker_sentinel"
+  input_stream: "ANCHORS:initial_anchor_data"
+  output_stream: "ANCHORS:tracked_anchor_data"
+}
+
+# Concatenates all transformations to generate model matrices for the OpenGL
+# animation overlay calculator.
+node {
+  calculator: "MatricesManagerCalculator"
+  input_stream: "ANCHORS:tracked_anchor_data"
+  input_stream: "IMU_ROTATION:imu_rotation_matrix"
+  input_stream: "USER_ROTATIONS:user_rotation_data"
+  input_stream: "USER_SCALINGS:user_scaling_data"
+  input_stream: "RENDER_DATA:sticker_render_data"
+  input_stream: "GIF_ASPECT_RATIO:gif_aspect_ratio"
+  output_stream: "MATRICES:0:gif_matrices"
+  output_stream: "MATRICES:1:asset_3d_matrices"
+  input_side_packet: "FOV:vertical_fov_radians"
+  input_side_packet: "ASPECT_RATIO:aspect_ratio"
+}
+
+# Renders the final 3d stickers and overlays them on input image.
+node {
+  calculator: "GlAnimationOverlayCalculator"
+  input_stream: "VIDEO:input_video"
+  input_stream: "MODEL_MATRICES:gif_matrices"
+  input_stream: "TEXTURE:gif_texture"
+  input_side_packet: "ANIMATION_ASSET:gif_asset_name"
+  output_stream: "asset_gif_rendered"
+}
+
+# Renders the final 3d stickers and overlays them on top of the input image.
+node {
+  calculator: "GlAnimationOverlayCalculator"
+  input_stream: "VIDEO:asset_gif_rendered"
+  input_stream: "MODEL_MATRICES:asset_3d_matrices"
+  input_side_packet: "TEXTURE:texture_3d"
+  input_side_packet: "ANIMATION_ASSET:asset_3d"
+  output_stream: "output_video"
+}
diff --git a/mediapipe/graphs/instant_motion_tracking/subgraphs/BUILD b/mediapipe/graphs/instant_motion_tracking/subgraphs/BUILD
new file mode 100644
index 0000000..cd1561b
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/subgraphs/BUILD
@@ -0,0 +1,32 @@
+# Copyright 2020 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "region_tracking",
+    graph = "region_tracking.pbtxt",
+    register_as = "RegionTrackingSubgraph",
+    deps = [
+        "//mediapipe/graphs/instant_motion_tracking/calculators:tracked_anchor_manager_calculator",
+        "//mediapipe/graphs/tracking/subgraphs:box_tracking_gpu",
+    ],
+)
diff --git a/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt b/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt
new file mode 100644
index 0000000..f8ef3ad
--- /dev/null
+++ b/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt
@@ -0,0 +1,47 @@
+# Copyright 2020 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# MediaPipe graph that performs region tracking on initial anchor positions
+# provided by the application
+
+# Images in/out of graph with tracked and scaled normalized anchor data
+type: "RegionTrackingSubgraph"
+input_stream: "VIDEO:input_video"
+input_stream: "SENTINEL:sticker_sentinel"
+input_stream: "ANCHORS:initial_anchor_data"
+output_stream: "ANCHORS:tracked_scaled_anchor_data"
+
+# Manages the anchors and tracking if user changes/adds/deletes anchors
+node {
+ calculator: "TrackedAnchorManagerCalculator"
+ input_stream: "SENTINEL:sticker_sentinel"
+ input_stream: "ANCHORS:initial_anchor_data"
+ input_stream: "BOXES:boxes"
+ input_stream_info: {
+   tag_index: 'BOXES'
+   back_edge: true
+ }
+ output_stream: "START_POS:start_pos"
+ output_stream: "CANCEL_ID:cancel_object_id"
+ output_stream: "ANCHORS:tracked_scaled_anchor_data"
+}
+
+# Subgraph performs anchor placement and tracking
+node {
+  calculator: "BoxTrackingSubgraphGpu"
+  input_stream: "VIDEO:input_video"
+  input_stream: "BOXES:start_pos"
+  input_stream: "CANCEL_ID:cancel_object_id"
+  output_stream: "BOXES:boxes"
+}
diff --git a/mediapipe/graphs/iris_tracking/BUILD b/mediapipe/graphs/iris_tracking/BUILD
new file mode 100644
index 0000000..86e667b
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/BUILD
@@ -0,0 +1,86 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "iris_depth_cpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_file_properties_calculator",
+        "//mediapipe/calculators/image:opencv_encoded_image_to_image_frame_calculator",
+        "//mediapipe/calculators/image:opencv_image_encoder_calculator",
+        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
+        "//mediapipe/graphs/iris_tracking/subgraphs:iris_and_depth_renderer_cpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
+        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_cpu",
+    ],
+)
+
+cc_library(
+    name = "iris_tracking_cpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
+        "//mediapipe/graphs/iris_tracking/subgraphs:iris_renderer_cpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
+        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_cpu",
+    ],
+)
+
+cc_library(
+    name = "iris_tracking_cpu_video_input_deps",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
+        "//mediapipe/graphs/iris_tracking/subgraphs:iris_renderer_cpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
+        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_cpu",
+    ],
+)
+
+cc_library(
+    name = "iris_tracking_gpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
+        "//mediapipe/graphs/iris_tracking/subgraphs:iris_and_depth_renderer_gpu",
+        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
+        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "iris_tracking_gpu_binary_graph",
+    graph = "iris_tracking_gpu.pbtxt",
+    output_name = "iris_tracking_gpu.binarypb",
+    deps = [":iris_tracking_gpu_deps"],
+)
diff --git a/mediapipe/graphs/iris_tracking/calculators/BUILD b/mediapipe/graphs/iris_tracking/calculators/BUILD
new file mode 100644
index 0000000..3a3d57a
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/calculators/BUILD
@@ -0,0 +1,107 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
+
+licenses(["notice"])
+
+proto_library(
+    name = "iris_to_render_data_calculator_proto",
+    srcs = ["iris_to_render_data_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_proto",
+        "//mediapipe/util:color_proto",
+        "//mediapipe/util:render_data_proto",
+    ],
+)
+
+mediapipe_cc_proto_library(
+    name = "iris_to_render_data_calculator_cc_proto",
+    srcs = ["iris_to_render_data_calculator.proto"],
+    cc_deps = [
+        "//mediapipe/framework:calculator_cc_proto",
+        "//mediapipe/util:color_cc_proto",
+        "//mediapipe/util:render_data_cc_proto",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [":iris_to_render_data_calculator_proto"],
+)
+
+cc_library(
+    name = "iris_to_render_data_calculator",
+    srcs = ["iris_to_render_data_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":iris_to_render_data_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/util:color_cc_proto",
+        "//mediapipe/util:render_data_cc_proto",
+        "@com_google_absl//absl/strings",
+    ],
+    alwayslink = 1,
+)
+
+proto_library(
+    name = "iris_to_depth_calculator_proto",
+    srcs = ["iris_to_depth_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_cc_proto_library(
+    name = "iris_to_depth_calculator_cc_proto",
+    srcs = ["iris_to_depth_calculator.proto"],
+    cc_deps = [
+        "//mediapipe/framework:calculator_cc_proto",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [":iris_to_depth_calculator_proto"],
+)
+
+cc_library(
+    name = "iris_to_depth_calculator",
+    srcs = ["iris_to_depth_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":iris_to_depth_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:image_file_properties_cc_proto",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "@com_google_absl//absl/strings",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "update_face_landmarks_calculator",
+    srcs = ["update_face_landmarks_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:image_file_properties_cc_proto",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "@com_google_absl//absl/strings",
+    ],
+    alwayslink = 1,
+)
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.cc b/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.cc
new file mode 100644
index 0000000..3522274
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.cc
@@ -0,0 +1,245 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cmath>
+#include <memory>
+
+#include "absl/strings/str_cat.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/image_file_properties.pb.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kIrisTag[] = "IRIS";
+constexpr char kImageSizeTag[] = "IMAGE_SIZE";
+constexpr char kFocalLengthPixelTag[] = "FOCAL_LENGTH";
+constexpr char kImageFilePropertiesTag[] = "IMAGE_FILE_PROPERTIES";
+constexpr char kLeftIrisDepthTag[] = "LEFT_IRIS_DEPTH_MM";
+constexpr char kRightIrisDepthTag[] = "RIGHT_IRIS_DEPTH_MM";
+constexpr int kNumIrisLandmarksPerEye = 5;
+constexpr float kDepthWeightUpdate = 0.1;
+// Avergae fixed iris size across human beings.
+constexpr float kIrisSizeInMM = 11.8;
+
+inline float GetDepth(float x0, float y0, float x1, float y1) {
+  return std::sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
+}
+
+inline float GetLandmarkDepth(const NormalizedLandmark& ld0,
+                              const NormalizedLandmark& ld1,
+                              const std::pair<int, int>& image_size) {
+  return GetDepth(ld0.x() * image_size.first, ld0.y() * image_size.second,
+                  ld1.x() * image_size.first, ld1.y() * image_size.second);
+}
+
+float CalculateIrisDiameter(const NormalizedLandmarkList& landmarks,
+                            const std::pair<int, int>& image_size) {
+  const float dist_vert = GetLandmarkDepth(landmarks.landmark(1),
+                                           landmarks.landmark(2), image_size);
+  const float dist_hori = GetLandmarkDepth(landmarks.landmark(3),
+                                           landmarks.landmark(4), image_size);
+  return (dist_hori + dist_vert) / 2.0f;
+}
+
+float CalculateDepth(const NormalizedLandmark& center, float focal_length,
+                     float iris_size, float img_w, float img_h) {
+  std::pair<float, float> origin{img_w / 2.f, img_h / 2.f};
+  const auto y = GetDepth(origin.first, origin.second, center.x() * img_w,
+                          center.y() * img_h);
+  const auto x = std::sqrt(focal_length * focal_length + y * y);
+  const auto depth = kIrisSizeInMM * x / iris_size;
+  return depth;
+}
+
+}  // namespace
+
+// Estimates depth from iris to camera given focal length and image size.
+//
+// Usage example:
+// node {
+//   calculator: "IrisToDepthCalculator"
+//   # A NormalizedLandmarkList contains landmarks for both iris.
+//   input_stream: "IRIS:iris_landmarks"
+//   input_stream: "IMAGE_SIZE:image_size"
+//   # Note: Only one of FOCAL_LENGTH or IMAGE_FILE_PROPERTIES is necessary
+//   # to get focal length in pixels. Sending focal length in pixels to
+//   # this calculator is optional.
+//   input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
+//   # OR
+//   input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
+//   output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+//   output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+// }
+class IrisToDepthCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->Inputs().Tag(kIrisTag).Set<NormalizedLandmarkList>();
+    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
+
+    // Only one of kFocalLengthPixelTag or kImageFilePropertiesTag must exist
+    // if they are present.
+    RET_CHECK(!(cc->InputSidePackets().HasTag(kFocalLengthPixelTag) &&
+                cc->InputSidePackets().HasTag(kImageFilePropertiesTag)));
+    if (cc->InputSidePackets().HasTag(kFocalLengthPixelTag)) {
+      cc->InputSidePackets().Tag(kFocalLengthPixelTag).SetAny();
+    }
+    if (cc->InputSidePackets().HasTag(kImageFilePropertiesTag)) {
+      cc->InputSidePackets()
+          .Tag(kImageFilePropertiesTag)
+          .Set<ImageFileProperties>();
+    }
+    if (cc->Outputs().HasTag(kLeftIrisDepthTag)) {
+      cc->Outputs().Tag(kLeftIrisDepthTag).Set<float>();
+    }
+    if (cc->Outputs().HasTag(kRightIrisDepthTag)) {
+      cc->Outputs().Tag(kRightIrisDepthTag).Set<float>();
+    }
+    return absl::OkStatus();
+  }
+
+  absl::Status Open(CalculatorContext* cc) override;
+
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  float focal_length_pixels_ = -1.f;
+  // TODO: Consolidate the logic when switching to input stream for
+  // focal length.
+  bool compute_depth_from_iris_ = false;
+  float smoothed_left_depth_mm_ = -1.f;
+  float smoothed_right_depth_mm_ = -1.f;
+
+  void GetLeftIris(const NormalizedLandmarkList& lds,
+                   NormalizedLandmarkList* iris);
+  void GetRightIris(const NormalizedLandmarkList& lds,
+                    NormalizedLandmarkList* iris);
+  ::mediapipe::IrisToDepthCalculatorOptions options_;
+};
+REGISTER_CALCULATOR(IrisToDepthCalculator);
+
+absl::Status IrisToDepthCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  if (cc->InputSidePackets().HasTag(kFocalLengthPixelTag)) {
+#if defined(__APPLE__)
+    focal_length_pixels_ = *cc->InputSidePackets()
+                                .Tag(kFocalLengthPixelTag)
+                                .Get<std::unique_ptr<float>>();
+#else
+    focal_length_pixels_ =
+        cc->InputSidePackets().Tag(kFocalLengthPixelTag).Get<float>();
+#endif
+    compute_depth_from_iris_ = true;
+  } else if (cc->InputSidePackets().HasTag(kImageFilePropertiesTag)) {
+    const auto& properties = cc->InputSidePackets()
+                                 .Tag(kImageFilePropertiesTag)
+                                 .Get<ImageFileProperties>();
+    focal_length_pixels_ = properties.focal_length_pixels();
+    compute_depth_from_iris_ = true;
+  }
+
+  options_ = cc->Options<::mediapipe::IrisToDepthCalculatorOptions>();
+  return absl::OkStatus();
+}
+
+absl::Status IrisToDepthCalculator::Process(CalculatorContext* cc) {
+  // Only process if there's input landmarks.
+  if (cc->Inputs().Tag(kIrisTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+
+  const auto& iris_landmarks =
+      cc->Inputs().Tag(kIrisTag).Get<NormalizedLandmarkList>();
+  RET_CHECK_EQ(iris_landmarks.landmark_size(), kNumIrisLandmarksPerEye * 2)
+      << "Wrong number of iris landmarks";
+
+  std::pair<int, int> image_size;
+  RET_CHECK(!cc->Inputs().Tag(kImageSizeTag).IsEmpty());
+  image_size = cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
+
+  auto left_iris = absl::make_unique<NormalizedLandmarkList>();
+  auto right_iris = absl::make_unique<NormalizedLandmarkList>();
+  GetLeftIris(iris_landmarks, left_iris.get());
+  GetRightIris(iris_landmarks, right_iris.get());
+
+  const auto left_iris_size = CalculateIrisDiameter(*left_iris, image_size);
+  const auto right_iris_size = CalculateIrisDiameter(*right_iris, image_size);
+
+#if defined(__APPLE__)
+  if (cc->InputSidePackets().HasTag(kFocalLengthPixelTag)) {
+    focal_length_pixels_ = *cc->InputSidePackets()
+                                .Tag(kFocalLengthPixelTag)
+                                .Get<std::unique_ptr<float>>();
+  }
+#endif
+
+  if (compute_depth_from_iris_ && focal_length_pixels_ > 0) {
+    const auto left_depth =
+        CalculateDepth(left_iris->landmark(0), focal_length_pixels_,
+                       left_iris_size, image_size.first, image_size.second);
+    const auto right_depth =
+        CalculateDepth(right_iris->landmark(0), focal_length_pixels_,
+                       right_iris_size, image_size.first, image_size.second);
+    smoothed_left_depth_mm_ =
+        smoothed_left_depth_mm_ < 0 || std::isinf(smoothed_left_depth_mm_)
+            ? left_depth
+            : smoothed_left_depth_mm_ * (1 - kDepthWeightUpdate) +
+                  left_depth * kDepthWeightUpdate;
+    smoothed_right_depth_mm_ =
+        smoothed_right_depth_mm_ < 0 || std::isinf(smoothed_right_depth_mm_)
+            ? right_depth
+            : smoothed_right_depth_mm_ * (1 - kDepthWeightUpdate) +
+                  right_depth * kDepthWeightUpdate;
+
+    if (cc->Outputs().HasTag(kLeftIrisDepthTag)) {
+      cc->Outputs()
+          .Tag(kLeftIrisDepthTag)
+          .AddPacket(MakePacket<float>(smoothed_left_depth_mm_)
+                         .At(cc->InputTimestamp()));
+    }
+    if (cc->Outputs().HasTag(kRightIrisDepthTag)) {
+      cc->Outputs()
+          .Tag(kRightIrisDepthTag)
+          .AddPacket(MakePacket<float>(smoothed_right_depth_mm_)
+                         .At(cc->InputTimestamp()));
+    }
+  }
+  return absl::OkStatus();
+}
+
+void IrisToDepthCalculator::GetLeftIris(const NormalizedLandmarkList& lds,
+                                        NormalizedLandmarkList* iris) {
+  // Center, top, bottom, left, right
+  *iris->add_landmark() = lds.landmark(options_.left_iris_center_index());
+  *iris->add_landmark() = lds.landmark(options_.left_iris_top_index());
+  *iris->add_landmark() = lds.landmark(options_.left_iris_bottom_index());
+  *iris->add_landmark() = lds.landmark(options_.left_iris_left_index());
+  *iris->add_landmark() = lds.landmark(options_.left_iris_right_index());
+}
+
+void IrisToDepthCalculator::GetRightIris(const NormalizedLandmarkList& lds,
+                                         NormalizedLandmarkList* iris) {
+  // Center, top, bottom, left, right
+  *iris->add_landmark() = lds.landmark(options_.right_iris_center_index());
+  *iris->add_landmark() = lds.landmark(options_.right_iris_top_index());
+  *iris->add_landmark() = lds.landmark(options_.right_iris_bottom_index());
+  *iris->add_landmark() = lds.landmark(options_.right_iris_left_index());
+  *iris->add_landmark() = lds.landmark(options_.right_iris_right_index());
+}
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.proto b/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.proto
new file mode 100644
index 0000000..786cd30
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.proto
@@ -0,0 +1,39 @@
+// Copyright 2019 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message IrisToDepthCalculatorOptions {
+  extend CalculatorOptions {
+    optional IrisToDepthCalculatorOptions ext = 303429002;
+  }
+
+  // Indices of correspondent left iris landmarks in input stream.
+  optional int32 left_iris_center_index = 1 [default = 0];
+  optional int32 left_iris_top_index = 2 [default = 2];
+  optional int32 left_iris_bottom_index = 3 [default = 4];
+  optional int32 left_iris_left_index = 4 [default = 3];
+  optional int32 left_iris_right_index = 5 [default = 1];
+
+  // Indices of correspondent right iris landmarks in input stream.
+  optional int32 right_iris_center_index = 6 [default = 5];
+  optional int32 right_iris_top_index = 7 [default = 7];
+  optional int32 right_iris_bottom_index = 8 [default = 9];
+  optional int32 right_iris_left_index = 9 [default = 6];
+  optional int32 right_iris_right_index = 10 [default = 8];
+}
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.cc b/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.cc
new file mode 100644
index 0000000..c19db2a
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.cc
@@ -0,0 +1,318 @@
+// Copyright 2019 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cmath>
+#include <memory>
+
+#include "absl/strings/str_cat.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.pb.h"
+#include "mediapipe/util/color.pb.h"
+#include "mediapipe/util/render_data.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kIrisTag[] = "IRIS";
+constexpr char kRenderDataTag[] = "RENDER_DATA";
+constexpr char kImageSizeTag[] = "IMAGE_SIZE";
+constexpr char kLeftIrisDepthTag[] = "LEFT_IRIS_DEPTH_MM";
+constexpr char kRightIrisDepthTag[] = "RIGHT_IRIS_DEPTH_MM";
+constexpr char kOvalLabel[] = "OVAL";
+constexpr float kFontHeightScale = 1.5f;
+constexpr int kNumIrisLandmarksPerEye = 5;
+// TODO: Source.
+constexpr float kIrisSizeInMM = 11.8;
+
+inline void SetColor(RenderAnnotation* annotation, const Color& color) {
+  annotation->mutable_color()->set_r(color.r());
+  annotation->mutable_color()->set_g(color.g());
+  annotation->mutable_color()->set_b(color.b());
+}
+
+inline float GetDepth(float x0, float y0, float x1, float y1) {
+  return std::sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
+}
+
+inline float GetLandmarkDepth(const NormalizedLandmark& ld0,
+                              const NormalizedLandmark& ld1,
+                              const std::pair<int, int>& image_size) {
+  return GetDepth(ld0.x() * image_size.first, ld0.y() * image_size.second,
+                  ld1.x() * image_size.first, ld1.y() * image_size.second);
+}
+
+float CalculateIrisDiameter(const NormalizedLandmarkList& landmarks,
+                            const std::pair<int, int>& image_size) {
+  const float dist_vert = GetLandmarkDepth(landmarks.landmark(1),
+                                           landmarks.landmark(2), image_size);
+  const float dist_hori = GetLandmarkDepth(landmarks.landmark(3),
+                                           landmarks.landmark(4), image_size);
+  return (dist_hori + dist_vert) / 2.0f;
+}
+
+float CalculateDepth(const NormalizedLandmark& center, float focal_length,
+                     float iris_size, float img_w, float img_h) {
+  std::pair<float, float> origin{img_w / 2.f, img_h / 2.f};
+  const auto y = GetDepth(origin.first, origin.second, center.x() * img_w,
+                          center.y() * img_h);
+  const auto x = std::sqrt(focal_length * focal_length + y * y);
+  const auto depth = kIrisSizeInMM * x / iris_size;
+  return depth;
+}
+
+}  // namespace
+
+// Converts iris landmarks to render data and estimates depth from the camera if
+// focal length and image size. The depth will be rendered as part of the render
+// data on the frame.
+//
+// Usage example:
+// node {
+//   calculator: "IrisToRenderDataCalculator"
+//   input_stream: "IRIS:iris_landmarks"
+//   input_stream: "IMAGE_SIZE:image_size"
+//   # Note: Only one of FOCAL_LENGTH or IMAGE_FILE_PROPERTIES is necessary
+//   # to get focal length in pixels. Sending focal length in pixels to
+//   # this calculator is optional.
+//   input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
+//   # OR
+//   input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
+//   output_stream: "RENDER_DATA:iris_render_data"
+//   output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+//   output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+//   node_options: {
+//     [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
+//       color { r: 255 g: 255 b: 255 }
+//       thickness: 2.0
+//       font_height_px: 50
+//       horizontal_offset_px: 200
+//       vertical_offset_px: 200
+//       location: TOP_LEFT
+//     }
+//   }
+// }
+class IrisToRenderDataCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->Inputs().Tag(kIrisTag).Set<NormalizedLandmarkList>();
+    cc->Outputs().Tag(kRenderDataTag).Set<RenderData>();
+    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
+
+    if (cc->Inputs().HasTag(kLeftIrisDepthTag)) {
+      cc->Inputs().Tag(kLeftIrisDepthTag).Set<float>();
+    }
+    if (cc->Inputs().HasTag(kRightIrisDepthTag)) {
+      cc->Inputs().Tag(kRightIrisDepthTag).Set<float>();
+    }
+    return absl::OkStatus();
+  }
+
+  absl::Status Open(CalculatorContext* cc) override;
+
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  void RenderIris(const NormalizedLandmarkList& iris_landmarks,
+                  const IrisToRenderDataCalculatorOptions& options,
+                  const std::pair<int, int>& image_size, float iris_size,
+                  RenderData* render_data);
+  void GetLeftIris(const NormalizedLandmarkList& lds,
+                   NormalizedLandmarkList* iris);
+  void GetRightIris(const NormalizedLandmarkList& lds,
+                    NormalizedLandmarkList* iris);
+
+  void AddTextRenderData(const IrisToRenderDataCalculatorOptions& options,
+                         const std::pair<int, int>& image_size,
+                         const std::vector<std::string>& lines,
+                         RenderData* render_data);
+
+  static RenderAnnotation* AddOvalRenderData(
+      const IrisToRenderDataCalculatorOptions& options,
+      RenderData* render_data);
+  static RenderAnnotation* AddPointRenderData(
+      const IrisToRenderDataCalculatorOptions& options,
+      RenderData* render_data);
+};
+REGISTER_CALCULATOR(IrisToRenderDataCalculator);
+
+absl::Status IrisToRenderDataCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  return absl::OkStatus();
+}
+
+absl::Status IrisToRenderDataCalculator::Process(CalculatorContext* cc) {
+  // Only process if there's input landmarks.
+  if (cc->Inputs().Tag(kIrisTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+  const auto& options =
+      cc->Options<::mediapipe::IrisToRenderDataCalculatorOptions>();
+
+  const auto& iris_landmarks =
+      cc->Inputs().Tag(kIrisTag).Get<NormalizedLandmarkList>();
+  RET_CHECK_EQ(iris_landmarks.landmark_size(), kNumIrisLandmarksPerEye * 2)
+      << "Wrong number of iris landmarks";
+
+  std::pair<int, int> image_size;
+  RET_CHECK(!cc->Inputs().Tag(kImageSizeTag).IsEmpty());
+  image_size = cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
+
+  auto render_data = absl::make_unique<RenderData>();
+  auto left_iris = absl::make_unique<NormalizedLandmarkList>();
+  auto right_iris = absl::make_unique<NormalizedLandmarkList>();
+  GetLeftIris(iris_landmarks, left_iris.get());
+  GetRightIris(iris_landmarks, right_iris.get());
+
+  const auto left_iris_size = CalculateIrisDiameter(*left_iris, image_size);
+  const auto right_iris_size = CalculateIrisDiameter(*right_iris, image_size);
+  RenderIris(*left_iris, options, image_size, left_iris_size,
+             render_data.get());
+  RenderIris(*right_iris, options, image_size, right_iris_size,
+             render_data.get());
+
+  std::vector<std::string> lines;
+  std::string line;
+  if (cc->Inputs().HasTag(kLeftIrisDepthTag) &&
+      !cc->Inputs().Tag(kLeftIrisDepthTag).IsEmpty()) {
+    const float left_iris_depth =
+        cc->Inputs().Tag(kLeftIrisDepthTag).Get<float>();
+    if (!std::isinf(left_iris_depth)) {
+      line = "Left : ";
+      absl::StrAppend(&line, ":", std::round(left_iris_depth / 10), " cm");
+      lines.emplace_back(line);
+    }
+  }
+  if (cc->Inputs().HasTag(kRightIrisDepthTag) &&
+      !cc->Inputs().Tag(kRightIrisDepthTag).IsEmpty()) {
+    const float right_iris_depth =
+        cc->Inputs().Tag(kRightIrisDepthTag).Get<float>();
+    if (!std::isinf(right_iris_depth)) {
+      line = "Right : ";
+      absl::StrAppend(&line, ":", std::round(right_iris_depth / 10), " cm");
+      lines.emplace_back(line);
+    }
+  }
+  AddTextRenderData(options, image_size, lines, render_data.get());
+
+  cc->Outputs()
+      .Tag(kRenderDataTag)
+      .Add(render_data.release(), cc->InputTimestamp());
+  return absl::OkStatus();
+}
+
+void IrisToRenderDataCalculator::AddTextRenderData(
+    const IrisToRenderDataCalculatorOptions& options,
+    const std::pair<int, int>& image_size,
+    const std::vector<std::string>& lines, RenderData* render_data) {
+  int label_baseline_px = options.vertical_offset_px();
+  float label_height_px =
+      std::ceil(options.font_height_px() * kFontHeightScale);
+  if (options.location() == IrisToRenderDataCalculatorOptions::TOP_LEFT) {
+    label_baseline_px += label_height_px;
+  } else if (options.location() ==
+             IrisToRenderDataCalculatorOptions::BOTTOM_LEFT) {
+    label_baseline_px += image_size.second - label_height_px * lines.size();
+  }
+  const auto label_left_px = options.horizontal_offset_px();
+  for (int i = 0; i < lines.size(); ++i) {
+    auto* label_annotation = render_data->add_render_annotations();
+    label_annotation->set_thickness(5);
+
+    label_annotation->mutable_color()->set_r(255);
+    label_annotation->mutable_color()->set_g(0);
+    label_annotation->mutable_color()->set_b(0);
+    //
+    auto* text = label_annotation->mutable_text();
+    text->set_display_text(lines[i]);
+    text->set_font_height(options.font_height_px());
+    text->set_left(label_left_px);
+    text->set_baseline(label_baseline_px + i * label_height_px);
+    text->set_font_face(options.font_face());
+  }
+}
+
+void IrisToRenderDataCalculator::RenderIris(
+    const NormalizedLandmarkList& iris_landmarks,
+    const IrisToRenderDataCalculatorOptions& options,
+    const std::pair<int, int>& image_size, float iris_size,
+    RenderData* render_data) {
+  auto* oval_data_render = AddOvalRenderData(options, render_data);
+  auto* oval_data = oval_data_render->mutable_oval();
+  const float iris_radius = iris_size / 2.f;
+  const auto& iris_center = iris_landmarks.landmark(0);
+
+  oval_data->mutable_rectangle()->set_top(iris_center.y() -
+                                          iris_radius / image_size.second);
+  oval_data->mutable_rectangle()->set_bottom(iris_center.y() +
+                                             iris_radius / image_size.second);
+  oval_data->mutable_rectangle()->set_left(iris_center.x() -
+                                           iris_radius / image_size.first);
+  oval_data->mutable_rectangle()->set_right(iris_center.x() +
+                                            iris_radius / image_size.first);
+  oval_data->mutable_rectangle()->set_normalized(true);
+
+  for (int i = 0; i < iris_landmarks.landmark_size(); ++i) {
+    const NormalizedLandmark& landmark = iris_landmarks.landmark(i);
+    auto* landmark_data_render = AddPointRenderData(options, render_data);
+    auto* landmark_data = landmark_data_render->mutable_point();
+    landmark_data->set_normalized(true);
+    landmark_data->set_x(landmark.x());
+    landmark_data->set_y(landmark.y());
+  }
+}
+
+void IrisToRenderDataCalculator::GetLeftIris(const NormalizedLandmarkList& lds,
+                                             NormalizedLandmarkList* iris) {
+  // Center, top, bottom, left, right
+  *iris->add_landmark() = lds.landmark(0);
+  *iris->add_landmark() = lds.landmark(2);
+  *iris->add_landmark() = lds.landmark(4);
+  *iris->add_landmark() = lds.landmark(3);
+  *iris->add_landmark() = lds.landmark(1);
+}
+
+void IrisToRenderDataCalculator::GetRightIris(const NormalizedLandmarkList& lds,
+                                              NormalizedLandmarkList* iris) {
+  // Center, top, bottom, left, right
+  *iris->add_landmark() = lds.landmark(5);
+  *iris->add_landmark() = lds.landmark(7);
+  *iris->add_landmark() = lds.landmark(9);
+  *iris->add_landmark() = lds.landmark(6);
+  *iris->add_landmark() = lds.landmark(8);
+}
+
+RenderAnnotation* IrisToRenderDataCalculator::AddOvalRenderData(
+    const IrisToRenderDataCalculatorOptions& options, RenderData* render_data) {
+  auto* oval_data_annotation = render_data->add_render_annotations();
+  oval_data_annotation->set_scene_tag(kOvalLabel);
+
+  SetColor(oval_data_annotation, options.oval_color());
+  oval_data_annotation->set_thickness(options.oval_thickness());
+  return oval_data_annotation;
+}
+
+RenderAnnotation* IrisToRenderDataCalculator::AddPointRenderData(
+    const IrisToRenderDataCalculatorOptions& options, RenderData* render_data) {
+  auto* landmark_data_annotation = render_data->add_render_annotations();
+  SetColor(landmark_data_annotation, options.landmark_color());
+  landmark_data_annotation->set_thickness(options.landmark_thickness());
+
+  return landmark_data_annotation;
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.proto b/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.proto
new file mode 100644
index 0000000..e0fc677
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.proto
@@ -0,0 +1,62 @@
+// Copyright 2019 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+import "mediapipe/util/color.proto";
+
+message IrisToRenderDataCalculatorOptions {
+  extend CalculatorOptions {
+    optional IrisToRenderDataCalculatorOptions ext = 289530040;
+  }
+
+  // Color of the oval.
+  optional Color oval_color = 1;
+  // Color of the landmarks.
+  optional Color landmark_color = 9;
+
+  // Thickness of the drawing of landmarks and iris oval.
+  optional double oval_thickness = 2 [default = 1.0];
+  optional double landmark_thickness = 10 [default = 1.0];
+
+  // The font height in absolute pixels.
+  optional int32 font_height_px = 3 [default = 50];
+
+  // The offset of the starting text in horizontal direction in absolute pixels.
+  optional int32 horizontal_offset_px = 7 [default = 0];
+  // The offset of the starting text in vertical direction in absolute pixels.
+  optional int32 vertical_offset_px = 8 [default = 0];
+
+  // Specifies the font for the text. Font must be one of the following from
+  // OpenCV:
+  // cv::FONT_HERSHEY_SIMPLEX (0)
+  // cv::FONT_HERSHEY_PLAIN (1)
+  // cv::FONT_HERSHEY_DUPLEX (2)
+  // cv::FONT_HERSHEY_COMPLEX (3)
+  // cv::FONT_HERSHEY_TRIPLEX (4)
+  // cv::FONT_HERSHEY_COMPLEX_SMALL (5)
+  // cv::FONT_HERSHEY_SCRIPT_SIMPLEX (6)
+  // cv::FONT_HERSHEY_SCRIPT_COMPLEX (7)
+  optional int32 font_face = 5 [default = 0];
+
+  // Label location.
+  enum Location {
+    TOP_LEFT = 0;
+    BOTTOM_LEFT = 1;
+  }
+  optional Location location = 6 [default = TOP_LEFT];
+}
diff --git a/mediapipe/graphs/iris_tracking/calculators/update_face_landmarks_calculator.cc b/mediapipe/graphs/iris_tracking/calculators/update_face_landmarks_calculator.cc
new file mode 100644
index 0000000..de9549a
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/calculators/update_face_landmarks_calculator.cc
@@ -0,0 +1,268 @@
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cmath>
+#include <memory>
+
+#include "absl/strings/str_cat.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kFaceLandmarksTag[] = "FACE_LANDMARKS";
+constexpr char kNewEyeLandmarksTag[] = "NEW_EYE_LANDMARKS";
+constexpr char kUpdatedFaceLandmarksTag[] = "UPDATED_FACE_LANDMARKS";
+
+constexpr int kNumFaceLandmarks = 468;
+// 71 landamrks for left eye and 71 landmarks for right eye.
+constexpr int kNumEyeLandmarks = 142;
+
+constexpr int kEyeLandmarkIndicesInFaceLandmarks[] = {
+    // Left eye
+    // eye lower contour
+    33,
+    7,
+    163,
+    144,
+    145,
+    153,
+    154,
+    155,
+    133,
+    // eye upper contour (excluding corners)
+    246,
+    161,
+    160,
+    159,
+    158,
+    157,
+    173,
+    // halo x2 lower contour
+    130,
+    25,
+    110,
+    24,
+    23,
+    22,
+    26,
+    112,
+    243,
+    // halo x2 upper contour (excluding corners)
+    247,
+    30,
+    29,
+    27,
+    28,
+    56,
+    190,
+    // halo x3 lower contour
+    226,
+    31,
+    228,
+    229,
+    230,
+    231,
+    232,
+    233,
+    244,
+    // halo x3 upper contour (excluding corners)
+    113,
+    225,
+    224,
+    223,
+    222,
+    221,
+    189,
+    // halo x4 upper contour (no lower because of mesh structure)
+    // or eyebrow inner contour
+    35,
+    124,
+    46,
+    53,
+    52,
+    65,
+    // halo x5 lower contour
+    143,
+    111,
+    117,
+    118,
+    119,
+    120,
+    121,
+    128,
+    245,
+    // halo x5 upper contour (excluding corners)
+    // or eyebrow outer contour
+    156,
+    70,
+    63,
+    105,
+    66,
+    107,
+    55,
+    193,
+
+    // Right eye
+    // eye lower contour
+    263,
+    249,
+    390,
+    373,
+    374,
+    380,
+    381,
+    382,
+    362,
+    // eye upper contour (excluding corners)
+    466,
+    388,
+    387,
+    386,
+    385,
+    384,
+    398,
+    // halo x2 lower contour
+    359,
+    255,
+    339,
+    254,
+    253,
+    252,
+    256,
+    341,
+    463,
+    // halo x2 upper contour (excluding corners)
+    467,
+    260,
+    259,
+    257,
+    258,
+    286,
+    414,
+    // halo x3 lower contour
+    446,
+    261,
+    448,
+    449,
+    450,
+    451,
+    452,
+    453,
+    464,
+    // halo x3 upper contour (excluding corners)
+    342,
+    445,
+    444,
+    443,
+    442,
+    441,
+    413,
+    // halo x4 upper contour (no lower because of mesh structure)
+    // or eyebrow inner contour
+    265,
+    353,
+    276,
+    283,
+    282,
+    295,
+    // halo x5 lower contour
+    372,
+    340,
+    346,
+    347,
+    348,
+    349,
+    350,
+    357,
+    465,
+    // halo x5 upper contour (excluding corners)
+    // or eyebrow outer contour
+    383,
+    300,
+    293,
+    334,
+    296,
+    336,
+    285,
+    417,
+};
+
+}  // namespace
+
+// Update face landmarks with new (e.g., refined) values. Currently only updates
+// landmarks around the eyes.
+//
+// Usage example:
+// node {
+//   calculator: "UpdateFaceLandmarksCalculator"
+//   input_stream: "NEW_EYE_LANDMARKS:new_eye_landmarks"
+//   input_stream: "FACE_LANDMARKS:face_landmarks"
+//   output_stream: "UPDATED_FACE_LANDMARKS:refine_face_landmarks"
+// }
+//
+class UpdateFaceLandmarksCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->Inputs().Tag(kFaceLandmarksTag).Set<NormalizedLandmarkList>();
+    cc->Inputs().Tag(kNewEyeLandmarksTag).Set<NormalizedLandmarkList>();
+
+    cc->Outputs().Tag(kUpdatedFaceLandmarksTag).Set<NormalizedLandmarkList>();
+
+    return absl::OkStatus();
+  }
+  absl::Status Open(CalculatorContext* cc) {
+    cc->SetOffset(TimestampDiff(0));
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) override;
+};
+REGISTER_CALCULATOR(UpdateFaceLandmarksCalculator);
+
+absl::Status UpdateFaceLandmarksCalculator::Process(CalculatorContext* cc) {
+  if (cc->Inputs().Tag(kFaceLandmarksTag).IsEmpty() ||
+      cc->Inputs().Tag(kNewEyeLandmarksTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+  const auto& face_landmarks =
+      cc->Inputs().Tag(kFaceLandmarksTag).Get<NormalizedLandmarkList>();
+  const auto& new_eye_landmarks =
+      cc->Inputs().Tag(kNewEyeLandmarksTag).Get<NormalizedLandmarkList>();
+
+  RET_CHECK_EQ(face_landmarks.landmark_size(), kNumFaceLandmarks)
+      << "Wrong number of face landmarks";
+  RET_CHECK_EQ(new_eye_landmarks.landmark_size(), kNumEyeLandmarks)
+      << "Wrong number of face landmarks";
+
+  auto refined_face_landmarks =
+      absl::make_unique<NormalizedLandmarkList>(face_landmarks);
+  for (int i = 0; i < kNumEyeLandmarks; ++i) {
+    const auto& refined_ld = new_eye_landmarks.landmark(i);
+    const int id = kEyeLandmarkIndicesInFaceLandmarks[i];
+    refined_face_landmarks->mutable_landmark(id)->set_x(refined_ld.x());
+    refined_face_landmarks->mutable_landmark(id)->set_y(refined_ld.y());
+    refined_face_landmarks->mutable_landmark(id)->set_z(refined_ld.z());
+    refined_face_landmarks->mutable_landmark(id)->set_visibility(
+        refined_ld.visibility());
+  }
+  cc->Outputs()
+      .Tag(kUpdatedFaceLandmarksTag)
+      .Add(refined_face_landmarks.release(), cc->InputTimestamp());
+
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/iris_tracking/iris_depth_cpu.pbtxt b/mediapipe/graphs/iris_tracking/iris_depth_cpu.pbtxt
new file mode 100644
index 0000000..3597e7f
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/iris_depth_cpu.pbtxt
@@ -0,0 +1,159 @@
+# MediaPipe graph that performs iris distance computation on desktop with
+# TensorFlow Lite on CPU.
+# Used in the example in
+# mediapipie/examples/desktop/iris_tracking:iris_depth_from_image_desktop.
+
+# Raw image bytes. (std::string)
+input_stream: "input_image_bytes"
+
+# Image with all the detections rendered. (ImageFrame)
+output_stream: "output_image"
+# Estimated depth in mm from the camera to the left iris of the face (if any) in
+# the image. (float)
+output_stream: "left_iris_depth_mm"
+# Estimated depth in mm from the camera to the right iris of the face (if any)
+# in the image. (float)
+output_stream: "right_iris_depth_mm"
+
+# Computes the focal length in pixels based on EXIF information stored in the
+# image file. The output is an ImageFileProperties object containing relevant
+# image EXIF information along with focal length in pixels.
+node {
+  calculator: "ImageFilePropertiesCalculator"
+  input_stream: "input_image_bytes"
+  output_side_packet: "image_file_properties"
+}
+
+# Converts a raw string with encoded image bytes into an ImageFrame object
+# via OpenCV so that it can be processed by downstream calculators.
+node {
+  calculator: "OpenCvEncodedImageToImageFrameCalculator"
+  input_stream: "input_image_bytes"
+  output_stream: "input_image"
+}
+
+# Defines how many faces to detect. Iris tracking currently only handles one
+# face (left and right eye), and therefore this should always be set to 1.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:num_faces"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+    }
+  }
+}
+
+# Detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontCpu"
+  input_stream: "IMAGE:input_image"
+  input_side_packet: "NUM_FACES:num_faces"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Gets the very first and only face from "multi_face_landmarks" vector.
+node {
+  calculator: "SplitNormalizedLandmarkListVectorCalculator"
+  input_stream: "multi_face_landmarks"
+  output_stream: "face_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets the very first and only face rect from "face_rects_from_landmarks"
+# vector.
+node {
+  calculator: "SplitNormalizedRectVectorCalculator"
+  input_stream: "face_rects_from_landmarks"
+  output_stream: "face_rect"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets two landmarks which define left eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "left_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 33 end: 34 }
+      ranges: { begin: 133 end: 134 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Gets two landmarks which define right eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "right_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 362 end: 363 }
+      ranges: { begin: 263 end: 264 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
+node {
+  calculator: "IrisLandmarkLeftAndRightCpu"
+  input_stream: "IMAGE:input_image"
+  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
+  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
+}
+
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "left_eye_contour_landmarks"
+  input_stream: "right_eye_contour_landmarks"
+  output_stream: "refined_eye_landmarks"
+}
+
+node {
+  calculator: "UpdateFaceLandmarksCalculator"
+  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
+  input_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
+}
+
+# Renders annotations and overlays them on top of the input images.
+node {
+  calculator: "IrisAndDepthRendererCpu"
+  input_stream: "IMAGE:input_image"
+  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
+  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
+  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
+  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
+  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
+  input_stream: "NORM_RECT:face_rect"
+  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
+  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
+  output_stream: "IRIS_LANDMARKS:iris_landmarks"
+  output_stream: "IMAGE:output_image"
+  output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+  output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+}
diff --git a/mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt b/mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt
new file mode 100644
index 0000000..c0a3857
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt
@@ -0,0 +1,142 @@
+# MediaPipe graph that performs iris tracking on desktop with TensorFlow Lite
+# on CPU.
+# Used in the example in
+# mediapipie/examples/desktop/iris_tracking:iris_tracking_cpu.
+
+# CPU image. (ImageFrame)
+input_stream: "input_video"
+
+# CPU image. (ImageFrame)
+output_stream: "output_video"
+# Face landmarks with iris. (NormalizedLandmarkList)
+output_stream: "face_landmarks_with_iris"
+
+# Defines how many faces to detect. Iris tracking currently only handles one
+# face (left and right eye), and therefore this should always be set to 1.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:num_faces"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+    }
+  }
+}
+
+# Detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontCpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Gets the very first and only face from "multi_face_landmarks" vector.
+node {
+  calculator: "SplitNormalizedLandmarkListVectorCalculator"
+  input_stream: "multi_face_landmarks"
+  output_stream: "face_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets the very first and only face rect from "face_rects_from_landmarks"
+# vector.
+node {
+  calculator: "SplitNormalizedRectVectorCalculator"
+  input_stream: "face_rects_from_landmarks"
+  output_stream: "face_rect"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets two landmarks which define left eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "left_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 33 end: 34 }
+      ranges: { begin: 133 end: 134 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Gets two landmarks which define right eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "right_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 362 end: 363 }
+      ranges: { begin: 263 end: 264 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
+node {
+  calculator: "IrisLandmarkLeftAndRightCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
+  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
+}
+
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "left_eye_contour_landmarks"
+  input_stream: "right_eye_contour_landmarks"
+  output_stream: "refined_eye_landmarks"
+}
+
+node {
+  calculator: "UpdateFaceLandmarksCalculator"
+  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
+  input_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
+}
+
+# Renders annotations and overlays them on top of the input images.
+node {
+  calculator: "IrisRendererCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
+  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
+  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
+  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
+  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
+  input_stream: "NORM_RECT:face_rect"
+  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
+  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "IRIS_LANDMARKS:iris_landmarks"
+  output_stream: "IMAGE:output_video"
+}
+
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "updated_face_landmarks"
+  input_stream: "iris_landmarks"
+  output_stream: "face_landmarks_with_iris"
+}
diff --git a/mediapipe/graphs/iris_tracking/iris_tracking_cpu_video_input.pbtxt b/mediapipe/graphs/iris_tracking/iris_tracking_cpu_video_input.pbtxt
new file mode 100644
index 0000000..82229bd
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/iris_tracking_cpu_video_input.pbtxt
@@ -0,0 +1,153 @@
+# MediaPipe graph that performs iris tracking on desktop with TensorFlow Lite
+# on CPU.
+
+# max_queue_size limits the number of packets enqueued on any input stream
+# by throttling inputs to the graph. This makes the graph only process one
+# frame per time.
+max_queue_size: 1
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+# Defines how many faces to detect. Iris tracking currently only handles one
+# face (left and right eye), and therefore this should always be set to 1.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:num_faces"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+    }
+  }
+}
+
+# Detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontCpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Gets the very first and only face from "multi_face_landmarks" vector.
+node {
+  calculator: "SplitNormalizedLandmarkListVectorCalculator"
+  input_stream: "multi_face_landmarks"
+  output_stream: "face_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets the very first and only face rect from "face_rects_from_landmarks"
+# vector.
+node {
+  calculator: "SplitNormalizedRectVectorCalculator"
+  input_stream: "face_rects_from_landmarks"
+  output_stream: "face_rect"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets two landmarks which define left eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "left_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 33 end: 34 }
+      ranges: { begin: 133 end: 134 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Gets two landmarks which define right eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "right_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 362 end: 363 }
+      ranges: { begin: 263 end: 264 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
+node {
+  calculator: "IrisLandmarkLeftAndRightCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
+  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
+}
+
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "left_eye_contour_landmarks"
+  input_stream: "right_eye_contour_landmarks"
+  output_stream: "refined_eye_landmarks"
+}
+
+node {
+  calculator: "UpdateFaceLandmarksCalculator"
+  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
+  input_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
+}
+
+# Renders annotations and overlays them on top of the input images.
+node {
+  calculator: "IrisRendererCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
+  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
+  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
+  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
+  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
+  input_stream: "NORM_RECT:face_rect"
+  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
+  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "IRIS_LANDMARKS:iris_landmarks"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt b/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt
new file mode 100644
index 0000000..505a951
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt
@@ -0,0 +1,163 @@
+# MediaPipe graph that performs iris tracking with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipie/examples/android/src/java/com/mediapipe/apps/iristrackinggpu and
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# GPU buffer. (GpuBuffer)
+output_stream: "output_video"
+# Face landmarks with iris. (NormalizedLandmarkList)
+output_stream: "face_landmarks_with_iris"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Defines how many faces to detect. Iris tracking currently only handles one
+# face (left and right eye), and therefore this should always be set to 1.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:num_faces"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+    }
+  }
+}
+
+# Detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Gets the very first and only face from "multi_face_landmarks" vector.
+node {
+  calculator: "SplitNormalizedLandmarkListVectorCalculator"
+  input_stream: "multi_face_landmarks"
+  output_stream: "face_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets the very first and only face rect from "face_rects_from_landmarks"
+# vector.
+node {
+  calculator: "SplitNormalizedRectVectorCalculator"
+  input_stream: "face_rects_from_landmarks"
+  output_stream: "face_rect"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Gets two landmarks which define left eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "left_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 33 end: 34 }
+      ranges: { begin: 133 end: 134 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Gets two landmarks which define right eye boundary.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "face_landmarks"
+  output_stream: "right_eye_boundary_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 362 end: 363 }
+      ranges: { begin: 263 end: 264 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
+node {
+  calculator: "IrisLandmarkLeftAndRightGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
+  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
+}
+
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "left_eye_contour_landmarks"
+  input_stream: "right_eye_contour_landmarks"
+  output_stream: "refined_eye_landmarks"
+}
+
+node {
+  calculator: "UpdateFaceLandmarksCalculator"
+  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
+  input_stream: "FACE_LANDMARKS:face_landmarks"
+  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
+}
+
+# Renders annotations and overlays them on top of the input images.
+node {
+  calculator: "IrisAndDepthRendererGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
+  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
+  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
+  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
+  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
+  input_stream: "NORM_RECT:face_rect"
+  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
+  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
+  output_stream: "IRIS_LANDMARKS:iris_landmarks"
+  output_stream: "IMAGE:output_video"
+}
+
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "updated_face_landmarks"
+  input_stream: "iris_landmarks"
+  output_stream: "face_landmarks_with_iris"
+}
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/BUILD b/mediapipe/graphs/iris_tracking/subgraphs/BUILD
new file mode 100644
index 0000000..d37c550
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/subgraphs/BUILD
@@ -0,0 +1,67 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "renderer_calculators",
+    deps = [
+        "//mediapipe/calculators/core:concatenate_normalized_landmark_list_calculator",
+        "//mediapipe/calculators/core:concatenate_vector_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+        "//mediapipe/graphs/face_mesh/calculators:face_landmarks_to_render_data_calculator",
+        "//mediapipe/graphs/iris_tracking/calculators:iris_to_render_data_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "iris_and_depth_renderer_gpu",
+    graph = "iris_and_depth_renderer_gpu.pbtxt",
+    register_as = "IrisAndDepthRendererGpu",
+    deps = [
+        ":renderer_calculators",
+        "//mediapipe/graphs/iris_tracking/calculators:iris_to_depth_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "iris_renderer_cpu",
+    graph = "iris_renderer_cpu.pbtxt",
+    register_as = "IrisRendererCpu",
+    deps = [
+        ":renderer_calculators",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "iris_and_depth_renderer_cpu",
+    graph = "iris_and_depth_renderer_cpu.pbtxt",
+    register_as = "IrisAndDepthRendererCpu",
+    deps = [
+        ":renderer_calculators",
+        "//mediapipe/graphs/iris_tracking/calculators:iris_to_depth_calculator",
+    ],
+)
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_cpu.pbtxt b/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_cpu.pbtxt
new file mode 100644
index 0000000..fad6d4a
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_cpu.pbtxt
@@ -0,0 +1,267 @@
+# MediaPipe iris tracking rendering subgraph.
+
+type: "IrisAndDepthRendererCpu"
+
+input_stream: "IMAGE:input_image"
+input_stream: "DETECTIONS:detections"
+input_stream: "FACE_LANDMARKS:face_landmarks"
+input_stream: "EYE_LANDMARKS_LEFT:all_left_eye_contour_landmarks"
+input_stream: "EYE_LANDMARKS_RIGHT:all_right_eye_contour_landmarks"
+input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
+input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
+input_stream: "NORM_RECT:rect"
+input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
+input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
+input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
+output_stream: "IRIS_LANDMARKS:iris_landmarks"
+output_stream: "IMAGE:output_image"
+output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "all_left_eye_contour_landmarks"
+  output_stream: "left_eye_contour_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 15 }
+    }
+  }
+}
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "all_right_eye_contour_landmarks"
+  output_stream: "right_eye_contour_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 15 }
+    }
+  }
+}
+
+# Concatenate iris landmarks from both eyes.
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "left_iris_landmarks"
+  input_stream: "right_iris_landmarks"
+  output_stream: "iris_landmarks"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "FaceLandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  output_stream: "RENDER_DATA:face_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 150 g: 0 b: 0 }
+      connection_color { r: 0 g: 150 b: 0 }
+      thickness: 2
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:input_image"
+  output_stream: "SIZE:image_size"
+}
+
+# Maps detection label IDs to the corresponding label text ("Face").
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "detections"
+  output_stream: "labeled_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label: "Face"
+    }
+  }
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:labeled_detections"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "RENDER_DATA:left_eye_contour_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 12
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 0
+      landmark_connections: 9
+      landmark_connections: 8
+      landmark_connections: 14
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 255 g: 0 b: 0 }
+      visualize_landmark_depth: false
+      thickness: 1.0
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "RENDER_DATA:right_eye_contour_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 12
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 0
+      landmark_connections: 9
+      landmark_connections: 8
+      landmark_connections: 14
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 255 g: 0 b: 0 }
+      visualize_landmark_depth: false
+      thickness: 1.0
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:rect"
+  output_stream: "RENDER_DATA:rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:right_eye_rect_from_landmarks"
+  output_stream: "RENDER_DATA:right_eye_rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:left_eye_rect_from_landmarks"
+  output_stream: "RENDER_DATA:left_eye_rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "IrisToDepthCalculator"
+  input_stream: "IRIS:iris_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
+  output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+  output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+}
+
+node {
+  calculator: "IrisToRenderDataCalculator"
+  input_stream: "IRIS:iris_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+  input_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+  output_stream: "RENDER_DATA:iris_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
+      oval_color { r: 0 g: 0 b: 255 }
+      landmark_color { r: 0 g: 255 b: 0 }
+      oval_thickness: 2.0
+      landmark_thickness: 1.0
+      font_height_px: 50
+      horizontal_offset_px: 200
+      vertical_offset_px: 200
+      location: TOP_LEFT
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_image"
+  input_stream: "detection_render_data"
+  input_stream: "face_landmarks_render_data"
+  input_stream: "right_eye_contour_landmarks_render_data"
+  input_stream: "left_eye_contour_landmarks_render_data"
+  input_stream: "iris_render_data"
+  output_stream: "IMAGE:output_image"
+}
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_gpu.pbtxt b/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_gpu.pbtxt
new file mode 100644
index 0000000..ba043d3
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_gpu.pbtxt
@@ -0,0 +1,270 @@
+# MediaPipe iris tracking rendering subgraph.
+
+type: "IrisAndDepthRendererGpu"
+
+input_stream: "IMAGE:input_image"
+input_stream: "DETECTIONS:detections"
+input_stream: "FACE_LANDMARKS:face_landmarks"
+input_stream: "EYE_LANDMARKS_LEFT:all_left_eye_contour_landmarks"
+input_stream: "EYE_LANDMARKS_RIGHT:all_right_eye_contour_landmarks"
+input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
+input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
+input_stream: "NORM_RECT:rect"
+input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
+input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
+input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
+output_stream: "IRIS_LANDMARKS:iris_landmarks"
+output_stream: "IMAGE:output_image"
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "all_left_eye_contour_landmarks"
+  output_stream: "left_eye_contour_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 15 }
+    }
+  }
+}
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "all_right_eye_contour_landmarks"
+  output_stream: "right_eye_contour_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 15 }
+    }
+  }
+}
+
+# Concatenate iris landmarks from both eyes.
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "left_iris_landmarks"
+  input_stream: "right_iris_landmarks"
+  output_stream: "iris_landmarks"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "FaceLandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  output_stream: "RENDER_DATA:face_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 150 g: 0 b: 0 }
+      connection_color { r: 0 g: 150 b: 0 }
+      thickness: 2
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  output_stream: "SIZE:image_size"
+}
+
+# Maps detection label IDs to the corresponding label text ("Face").
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "detections"
+  output_stream: "labeled_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label: "Face"
+    }
+  }
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:labeled_detections"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "RENDER_DATA:left_eye_contour_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 12
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 0
+      landmark_connections: 9
+      landmark_connections: 8
+      landmark_connections: 14
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 255 g: 0 b: 0 }
+      visualize_landmark_depth: false
+      thickness: 2.0
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "RENDER_DATA:right_eye_contour_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 12
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 0
+      landmark_connections: 9
+      landmark_connections: 8
+      landmark_connections: 14
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 255 g: 0 b: 0 }
+      visualize_landmark_depth: false
+      thickness: 2.0
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:rect"
+  output_stream: "RENDER_DATA:rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:right_eye_rect_from_landmarks"
+  output_stream: "RENDER_DATA:right_eye_rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:left_eye_rect_from_landmarks"
+  output_stream: "RENDER_DATA:left_eye_rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "IrisToDepthCalculator"
+  input_stream: "IRIS:iris_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
+  output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+  output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+}
+
+node {
+  calculator: "IrisToRenderDataCalculator"
+  input_stream: "IRIS:iris_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
+  input_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
+  output_stream: "RENDER_DATA:iris_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
+      oval_color { r: 0 g: 0 b: 255 }
+      landmark_color { r: 0 g: 255 b: 0 }
+      oval_thickness: 4.0
+      landmark_thickness: 2.0
+      font_height_px: 50
+      horizontal_offset_px: 200
+      vertical_offset_px: 200
+      location: TOP_LEFT
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  input_stream: "detection_render_data"
+  input_stream: "face_landmarks_render_data"
+  input_stream: "right_eye_contour_landmarks_render_data"
+  input_stream: "left_eye_contour_landmarks_render_data"
+  input_stream: "iris_render_data"
+  output_stream: "IMAGE_GPU:output_image"
+  node_options: {
+    [type.googleapis.com/mediapipe.AnnotationOverlayCalculatorOptions] {
+      gpu_scale_factor: 0.5
+    }
+  }
+}
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/iris_renderer_cpu.pbtxt b/mediapipe/graphs/iris_tracking/subgraphs/iris_renderer_cpu.pbtxt
new file mode 100644
index 0000000..81a3c90
--- /dev/null
+++ b/mediapipe/graphs/iris_tracking/subgraphs/iris_renderer_cpu.pbtxt
@@ -0,0 +1,254 @@
+# MediaPipe iris tracking rendering subgraph.
+
+type: "IrisRendererCpu"
+
+input_stream: "IMAGE:input_image"
+input_stream: "DETECTIONS:detections"
+input_stream: "FACE_LANDMARKS:face_landmarks"
+input_stream: "EYE_LANDMARKS_LEFT:all_left_eye_contour_landmarks"
+input_stream: "EYE_LANDMARKS_RIGHT:all_right_eye_contour_landmarks"
+input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
+input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
+input_stream: "NORM_RECT:rect"
+input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
+input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
+output_stream: "IRIS_LANDMARKS:iris_landmarks"
+output_stream: "IMAGE:output_image"
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "all_left_eye_contour_landmarks"
+  output_stream: "left_eye_contour_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 15 }
+    }
+  }
+}
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "all_right_eye_contour_landmarks"
+  output_stream: "right_eye_contour_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 15 }
+    }
+  }
+}
+
+# Concatenate iris landmarks from both eyes.
+node {
+  calculator: "ConcatenateNormalizedLandmarkListCalculator"
+  input_stream: "left_iris_landmarks"
+  input_stream: "right_iris_landmarks"
+  output_stream: "iris_landmarks"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "FaceLandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  output_stream: "RENDER_DATA:face_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 150 g: 0 b: 0 }
+      connection_color { r: 0 g: 150 b: 0 }
+      thickness: 2
+      visualize_landmark_depth: false
+    }
+  }
+}
+
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:input_image"
+  output_stream: "SIZE:image_size"
+}
+
+# Maps detection label IDs to the corresponding label text ("Face").
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "detections"
+  output_stream: "labeled_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label: "Face"
+    }
+  }
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:labeled_detections"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "RENDER_DATA:left_eye_contour_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 12
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 0
+      landmark_connections: 9
+      landmark_connections: 8
+      landmark_connections: 14
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 255 g: 0 b: 0 }
+      visualize_landmark_depth: false
+      thickness: 1.0
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "RENDER_DATA:right_eye_contour_landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 7
+      landmark_connections: 7
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 12
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 14
+      landmark_connections: 0
+      landmark_connections: 9
+      landmark_connections: 8
+      landmark_connections: 14
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 255 g: 0 b: 0 }
+      visualize_landmark_depth: false
+      thickness: 1.0
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:rect"
+  output_stream: "RENDER_DATA:rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:right_eye_rect_from_landmarks"
+  output_stream: "RENDER_DATA:right_eye_rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:left_eye_rect_from_landmarks"
+  output_stream: "RENDER_DATA:left_eye_rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "IrisToRenderDataCalculator"
+  input_stream: "IRIS:iris_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "RENDER_DATA:iris_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
+      oval_color { r: 0 g: 0 b: 255 }
+      landmark_color { r: 0 g: 255 b: 0 }
+      oval_thickness: 4.0
+      landmark_thickness: 2.0
+      font_height_px: 50
+      horizontal_offset_px: 200
+      vertical_offset_px: 200
+      location: TOP_LEFT
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_image"
+  input_stream: "detection_render_data"
+  input_stream: "face_landmarks_render_data"
+  input_stream: "right_eye_contour_landmarks_render_data"
+  input_stream: "left_eye_contour_landmarks_render_data"
+  input_stream: "iris_render_data"
+  output_stream: "IMAGE:output_image"
+}
diff --git a/mediapipe/graphs/media_sequence/BUILD b/mediapipe/graphs/media_sequence/BUILD
new file mode 100644
index 0000000..e989147
--- /dev/null
+++ b/mediapipe/graphs/media_sequence/BUILD
@@ -0,0 +1,47 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "clipped_images_from_file_at_24fps_calculators",
+    deps = [
+        "//mediapipe/calculators/core:packet_resampler_calculator",
+        "//mediapipe/calculators/image:opencv_image_encoder_calculator",
+        "//mediapipe/calculators/image:scale_image_calculator",
+        "//mediapipe/calculators/tensorflow:pack_media_sequence_calculator",
+        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
+        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+    ],
+)
+
+cc_library(
+    name = "tvl1_flow_and_rgb_from_file_calculators",
+    deps = [
+        "//mediapipe/calculators/core:packet_inner_join_calculator",
+        "//mediapipe/calculators/core:packet_resampler_calculator",
+        "//mediapipe/calculators/core:sequence_shift_calculator",
+        "//mediapipe/calculators/image:opencv_image_encoder_calculator",
+        "//mediapipe/calculators/image:scale_image_calculator",
+        "//mediapipe/calculators/tensorflow:pack_media_sequence_calculator",
+        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
+        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
+        "//mediapipe/calculators/video:flow_to_image_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:tvl1_optical_flow_calculator",
+    ],
+)
diff --git a/mediapipe/graphs/media_sequence/clipped_images_from_file_at_24fps.pbtxt b/mediapipe/graphs/media_sequence/clipped_images_from_file_at_24fps.pbtxt
new file mode 100644
index 0000000..e3c6a51
--- /dev/null
+++ b/mediapipe/graphs/media_sequence/clipped_images_from_file_at_24fps.pbtxt
@@ -0,0 +1,78 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Convert the string input into a decoded SequenceExample.
+node {
+  calculator: "StringToSequenceExampleCalculator"
+  input_side_packet: "STRING:input_sequence_example"
+  output_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+}
+
+# Unpack the data path and clip timing from the SequenceExample.
+node {
+  calculator: "UnpackMediaSequenceCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+  output_side_packet: "DATA_PATH:input_video_path"
+  output_side_packet: "RESAMPLER_OPTIONS:packet_resampler_options"
+  node_options: {
+    [type.googleapis.com/mediapipe.UnpackMediaSequenceCalculatorOptions]: {
+      base_packet_resampler_options: {
+        frame_rate: 24.0
+        base_timestamp: 0
+      }
+    }
+  }
+}
+
+# Decode the entire video.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:decoded_frames"
+}
+
+# Extract the subset of frames we want to keep.
+node {
+  calculator: "PacketResamplerCalculator"
+  input_stream: "decoded_frames"
+  output_stream: "sampled_frames"
+  input_side_packet: "OPTIONS:packet_resampler_options"
+}
+
+# Encode the images to store in the SequenceExample.
+node {
+  calculator: "OpenCvImageEncoderCalculator"
+  input_stream: "sampled_frames"
+  output_stream: "encoded_frames"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvImageEncoderCalculatorOptions]: {
+      quality: 80
+    }
+  }
+}
+
+# Store the images in the SequenceExample.
+node {
+  calculator: "PackMediaSequenceCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+  output_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
+  input_stream: "IMAGE:encoded_frames"
+}
+
+# Serialize the SequenceExample to a string for storage.
+node {
+  calculator: "StringToSequenceExampleCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
+  output_side_packet: "STRING:output_sequence_example"
+}
diff --git a/mediapipe/graphs/media_sequence/tvl1_flow_and_rgb_from_file.pbtxt b/mediapipe/graphs/media_sequence/tvl1_flow_and_rgb_from_file.pbtxt
new file mode 100644
index 0000000..032fc36
--- /dev/null
+++ b/mediapipe/graphs/media_sequence/tvl1_flow_and_rgb_from_file.pbtxt
@@ -0,0 +1,153 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Convert the string input into a decoded SequenceExample.
+node {
+  calculator: "StringToSequenceExampleCalculator"
+  input_side_packet: "STRING:input_sequence_example"
+  output_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+}
+
+# Unpack the data path and clip timing from the SequenceExample.
+node {
+  calculator: "UnpackMediaSequenceCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+  output_side_packet: "DATA_PATH:input_video_path"
+  output_side_packet: "RESAMPLER_OPTIONS:packet_resampler_options"
+  node_options: {
+    [type.googleapis.com/mediapipe.UnpackMediaSequenceCalculatorOptions]: {
+      base_packet_resampler_options: {
+        frame_rate: 25.0
+        base_timestamp: 0
+      }
+    }
+  }
+}
+
+# Decode the entire video.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:decoded_frames"
+}
+
+# Extract the subset of frames we want to keep.
+node {
+  calculator: "PacketResamplerCalculator"
+  input_stream: "decoded_frames"
+  output_stream: "sampled_frames"
+  input_side_packet: "OPTIONS:packet_resampler_options"
+}
+
+# Fit the images into the target size.
+node: {
+  calculator: "ScaleImageCalculator"
+  input_stream: "sampled_frames"
+  output_stream: "scaled_frames"
+  node_options: {
+    [type.googleapis.com/mediapipe.ScaleImageCalculatorOptions]: {
+      target_height: 256
+      preserve_aspect_ratio: true
+    }
+  }
+}
+
+# Shift the the timestamps of packets along a stream.
+# With a packet_offset of -1, the first packet will be dropped, the second will
+# be output with the timestamp of the first, the third with the timestamp of
+# the second, and so on.
+node: {
+  calculator: "SequenceShiftCalculator"
+  input_stream: "scaled_frames"
+  output_stream: "shifted_scaled_frames"
+  node_options: {
+    [type.googleapis.com/mediapipe.SequenceShiftCalculatorOptions]: {
+      packet_offset: -1
+    }
+  }
+}
+
+# Join the original input stream and the one that is shifted by one packet.
+node: {
+  calculator: "PacketInnerJoinCalculator"
+  input_stream: "scaled_frames"
+  input_stream: "shifted_scaled_frames"
+  output_stream: "first_frames"
+  output_stream: "second_frames"
+}
+
+# Compute the forward optical flow.
+node {
+  calculator: "Tvl1OpticalFlowCalculator"
+  input_stream: "FIRST_FRAME:first_frames"
+  input_stream: "SECOND_FRAME:second_frames"
+  output_stream: "FORWARD_FLOW:forward_flow"
+  max_in_flight: 32
+}
+
+# Convert an optical flow to be an image frame with 2 channels (v_x and v_y),
+# each channel is quantized to 0-255.
+node: {
+  calculator: "FlowToImageCalculator"
+  input_stream: "forward_flow"
+  output_stream: "flow_frames"
+  node_options: {
+    [type.googleapis.com/mediapipe.FlowToImageCalculatorOptions]: {
+      min_value: -20.0
+      max_value: 20.0
+    }
+  }
+}
+
+# Encode the optical flow images to store in the SequenceExample.
+node {
+  calculator: "OpenCvImageEncoderCalculator"
+  input_stream: "flow_frames"
+  output_stream: "encoded_flow_frames"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvImageEncoderCalculatorOptions]: {
+      quality: 100
+    }
+  }
+}
+
+# Encode the rgb images to store in the SequenceExample.
+node {
+  calculator: "OpenCvImageEncoderCalculator"
+  input_stream: "scaled_frames"
+  output_stream: "encoded_frames"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvImageEncoderCalculatorOptions]: {
+      quality: 100
+    }
+  }
+}
+
+# Store the images in the SequenceExample.
+node {
+  calculator: "PackMediaSequenceCalculator"
+  input_stream: "IMAGE:encoded_frames"
+  input_stream: "FORWARD_FLOW_ENCODED:encoded_flow_frames"
+  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+  output_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
+}
+
+# Serialize the SequenceExample to a string for storage.
+node {
+  calculator: "StringToSequenceExampleCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
+  output_side_packet: "STRING:output_sequence_example"
+}
+
+num_threads: 32
diff --git a/mediapipe/graphs/object_detection/BUILD b/mediapipe/graphs/object_detection/BUILD
new file mode 100644
index 0000000..ef53fd2
--- /dev/null
+++ b/mediapipe/graphs/object_detection/BUILD
@@ -0,0 +1,94 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
+        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
+    ],
+)
+
+cc_library(
+    name = "desktop_tensorflow_calculators",
+    deps = [
+        "//mediapipe/calculators/tensorflow:image_frame_to_tensor_calculator",
+        "//mediapipe/calculators/tensorflow:lapped_tensor_buffer_calculator",
+        "//mediapipe/calculators/tensorflow:object_detection_tensors_to_detections_calculator",
+        "//mediapipe/calculators/tensorflow:tensor_squeeze_dimensions_calculator",
+        "//mediapipe/calculators/tensorflow:tensorflow_inference_calculator",
+        "//mediapipe/calculators/tensorflow:tensorflow_session_from_saved_model_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+    ],
+)
+
+cc_library(
+    name = "desktop_tflite_calculators",
+    deps = [
+        "//mediapipe/calculators/core:concatenate_vector_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+    ],
+)
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+mediapipe_binary_graph(
+    name = "mobile_cpu_binary_graph",
+    graph = "object_detection_mobile_cpu.pbtxt",
+    output_name = "mobile_cpu.binarypb",
+    deps = [":mobile_calculators"],
+)
+
+mediapipe_binary_graph(
+    name = "mobile_gpu_binary_graph",
+    graph = "object_detection_mobile_gpu.pbtxt",
+    output_name = "mobile_gpu.binarypb",
+    deps = [":mobile_calculators"],
+)
diff --git a/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt b/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt
new file mode 100644
index 0000000..98b9fab
--- /dev/null
+++ b/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt
@@ -0,0 +1,174 @@
+# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
+# Used in the examples in
+# mediapipe/examples/desktop/object_detection:object_detection_cpu.
+
+# Images on CPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
+# generating the corresponding detections before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Transforms the input image on CPU to a 320x320 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the object
+# detection model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "IMAGE:transformed_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 320
+    }
+  }
+}
+
+# Converts the transformed input image on CPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE:transformed_input_video"
+  output_stream: "TENSORS:image_tensor"
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  node_options: {
+    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 320
+      input_size_width: 320
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TfLiteTensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
+      num_classes: 91
+      num_boxes: 2034
+      num_coords: 4
+      ignore_classes: 0
+      sigmoid_score: true
+      apply_exponential_on_box_size: true
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+      min_score_thresh: 0.6
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "detections"
+  output_stream: "filtered_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
+      min_suppression_threshold: 0.4
+      max_num_detections: 3
+      overlap_type: INTERSECTION_OVER_UNION
+      return_empty_detections: true
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "output_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
+    }
+  }
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt b/mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt
new file mode 100644
index 0000000..f12eeb6
--- /dev/null
+++ b/mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt
@@ -0,0 +1,130 @@
+# MediaPipe graph that performs object detection on desktop with TensorFlow
+# on CPU.
+# Used in the example in
+# mediapipie/examples/desktop/object_detection:object_detection_tensorflow.
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+# Converts the input image into an image tensor as a tensorflow::Tensor.
+node {
+  calculator: "ImageFrameToTensorCalculator"
+  input_stream: "input_video"
+  output_stream: "image_tensor"
+}
+
+# Generates a single side packet containing a TensorFlow session from a saved
+# model. The directory path that contains the saved model is specified in the
+# saved_model_path option, and the name of the saved model file has to be
+# "saved_model.pb".
+node {
+  calculator: "TensorFlowSessionFromSavedModelCalculator"
+  output_side_packet: "SESSION:object_detection_session"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowSessionFromSavedModelCalculatorOptions]: {
+      saved_model_path: "mediapipe/models/object_detection_saved_model"
+    }
+  }
+}
+
+# Runs a TensorFlow session (specified as an input side packet) that takes an
+# image tensor and outputs multiple tensors that describe the objects detected
+# in the image. The batch_size option is set to 1 to disable batching entirely.
+# Note that the particular TensorFlow model used in this session handles image
+# scaling internally before the object-detection inference, and therefore no
+# additional calculator for image transformation is needed in this MediaPipe
+# graph.
+node: {
+  calculator: "TensorFlowInferenceCalculator"
+  input_side_packet: "SESSION:object_detection_session"
+  input_stream: "INPUTS:image_tensor"
+  output_stream: "DETECTION_BOXES:detection_boxes_tensor"
+  output_stream: "DETECTION_CLASSES:detection_classes_tensor"
+  output_stream: "DETECTION_SCORES:detection_scores_tensor"
+  output_stream: "NUM_DETECTIONS:num_detections_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
+      batch_size: 1
+    }
+  }
+}
+
+# Decodes the detection tensors from the TensorFlow model into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "ObjectDetectionTensorsToDetectionsCalculator"
+  input_stream: "BOXES:detection_boxes_tensor"
+  input_stream: "SCORES:detection_scores_tensor"
+  input_stream: "CLASSES:detection_classes_tensor"
+  input_stream: "NUM_DETECTIONS:num_detections_tensor"
+  output_stream: "DETECTIONS:detections"
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "detections"
+  output_stream: "filtered_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
+      min_suppression_threshold: 0.4
+      min_score_threshold: 0.6
+      max_num_detections: 10
+      overlap_type: INTERSECTION_OVER_UNION
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "output_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
+    }
+  }
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt b/mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt
new file mode 100644
index 0000000..15aa2cd
--- /dev/null
+++ b/mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt
@@ -0,0 +1,180 @@
+# MediaPipe graph that performs object detection on desktop with TensorFlow Lite
+# on CPU.
+# Used in the example in
+# mediapipe/examples/desktop/object_detection:object_detection_tflite.
+
+# max_queue_size limits the number of packets enqueued on any input stream
+# by throttling inputs to the graph. This makes the graph only process one
+# frame per time.
+max_queue_size: 1
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+# Transforms the input image on CPU to a 320x320 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the object
+# detection model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "IMAGE:transformed_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 320
+    }
+  }
+}
+
+# Converts the transformed input image on CPU into an image tensor as a
+# TfLiteTensor. The zero_center option is set to true to normalize the
+# pixel values to [-1.f, 1.f] as opposed to [0.f, 1.f].
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE:transformed_input_video"
+  output_stream: "TENSORS:image_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
+      zero_center: true
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  node_options: {
+    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 320
+      input_size_width: 320
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TfLiteTensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
+      num_classes: 91
+      num_boxes: 2034
+      num_coords: 4
+      ignore_classes: 0
+      apply_exponential_on_box_size: true
+
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "detections"
+  output_stream: "filtered_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
+      min_suppression_threshold: 0.4
+      min_score_threshold: 0.6
+      max_num_detections: 5
+      overlap_type: INTERSECTION_OVER_UNION
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "output_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
+    }
+  }
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt b/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt
new file mode 100644
index 0000000..8256179
--- /dev/null
+++ b/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt
@@ -0,0 +1,193 @@
+# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/objectdetectioncpu and
+# mediapipe/examples/ios/objectdetectioncpu.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Transfers the input image from GPU to CPU memory for the purpose of
+# demonstrating a CPU-based pipeline. Note that the input image on GPU has the
+# origin defined at the bottom-left corner (OpenGL convention). As a result,
+# the transferred image on CPU also shares the same representation.
+node: {
+  calculator: "GpuBufferToImageFrameCalculator"
+  input_stream: "input_video"
+  output_stream: "input_video_cpu"
+}
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
+# generating the corresponding detections before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video_cpu"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video_cpu"
+}
+
+# Transforms the input image on CPU to a 320x320 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the object
+# detection model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:throttled_input_video_cpu"
+  output_stream: "IMAGE:transformed_input_video_cpu"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 320
+    }
+  }
+}
+
+# Converts the transformed input image on CPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE:transformed_input_video_cpu"
+  output_stream: "TENSORS:image_tensor"
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  node_options: {
+    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 320
+      input_size_width: 320
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TfLiteTensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
+      num_classes: 91
+      num_boxes: 2034
+      num_coords: 4
+      ignore_classes: 0
+      sigmoid_score: true
+      apply_exponential_on_box_size: true
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+      min_score_thresh: 0.6
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "detections"
+  output_stream: "filtered_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
+      min_suppression_threshold: 0.4
+      max_num_detections: 3
+      overlap_type: INTERSECTION_OVER_UNION
+      return_empty_detections: true
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "output_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
+    }
+  }
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:throttled_input_video_cpu"
+  input_stream: "render_data"
+  output_stream: "IMAGE:output_video_cpu"
+}
+
+# Transfers the annotated image from CPU back to GPU memory, to be sent out of
+# the graph.
+node: {
+  calculator: "ImageFrameToGpuBufferCalculator"
+  input_stream: "output_video_cpu"
+  output_stream: "output_video"
+}
diff --git a/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt b/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt
new file mode 100644
index 0000000..1ed66e8
--- /dev/null
+++ b/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt
@@ -0,0 +1,175 @@
+# MediaPipe graph that performs object detection with TensorFlow Lite on GPU.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/objectdetectiongpu and
+# mediapipe/examples/ios/objectdetectiongpu.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for
+# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
+# generating the corresponding detections before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images between this calculator and
+# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
+# from queuing up incoming images and data excessively, which leads to increased
+# latency and memory usage, unwanted in real-time mobile applications. It also
+# eliminates unnecessarily computation, e.g., a transformed image produced by
+# ImageTransformationCalculator may get dropped downstream if the subsequent
+# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
+# processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Transforms the input image on GPU to a 320x320 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the object
+# detection model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  output_stream: "IMAGE_GPU:transformed_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 320
+    }
+  }
+}
+
+# Converts the transformed input image on GPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE_GPU:transformed_input_video"
+  output_stream: "TENSORS_GPU:image_tensor"
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS_GPU:image_tensor"
+  output_stream: "TENSORS_GPU:detection_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  node_options: {
+    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 320
+      input_size_width: 320
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TfLiteTensorsToDetectionsCalculator"
+  input_stream: "TENSORS_GPU:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
+      num_classes: 91
+      num_boxes: 2034
+      num_coords: 4
+      ignore_classes: 0
+      sigmoid_score: true
+      apply_exponential_on_box_size: true
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+      min_score_thresh: 0.6
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "detections"
+  output_stream: "filtered_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
+      min_suppression_threshold: 0.4
+      max_num_detections: 3
+      overlap_type: INTERSECTION_OVER_UNION
+      return_empty_detections: true
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "output_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
+    }
+  }
+}
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "render_data"
+  output_stream: "IMAGE_GPU:output_video"
+}
diff --git a/mediapipe/graphs/object_detection_3d/BUILD b/mediapipe/graphs/object_detection_3d/BUILD
new file mode 100644
index 0000000..7ba00c0
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/BUILD
@@ -0,0 +1,80 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+exports_files(glob([
+    "*.pbtxt",
+]))
+
+cc_library(
+    name = "mobile_calculators",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_cropping_calculator",
+        "//mediapipe/graphs/object_detection_3d/calculators:annotations_to_model_matrices_calculator",
+        "//mediapipe/graphs/object_detection_3d/calculators:gl_animation_overlay_calculator",
+        "//mediapipe/modules/objectron:objectron_gpu",
+    ],
+)
+
+cc_library(
+    name = "mobile_calculators_1stage",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/calculators/core:packet_resampler_calculator",
+        "//mediapipe/calculators/image:image_cropping_calculator",
+        "//mediapipe/gpu:gl_scaler_calculator",
+        "//mediapipe/graphs/object_detection_3d/calculators:annotations_to_model_matrices_calculator",
+        "//mediapipe/graphs/object_detection_3d/calculators:gl_animation_overlay_calculator",
+        "//mediapipe/modules/objectron:objectron_detection_1stage_gpu",
+        "//mediapipe/modules/objectron:objectron_tracking_1stage_gpu",
+    ],
+)
+
+cc_library(
+    name = "desktop_cpu_calculators",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+        "//mediapipe/graphs/object_detection_3d/subgraphs:renderer_cpu",
+        "//mediapipe/modules/objectron:objectron_cpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "mobile_gpu_binary_graph",
+    graph = "object_occlusion_tracking.pbtxt",
+    output_name = "mobile_gpu_binary_graph.binarypb",
+    visibility = ["//visibility:public"],
+    deps = [":mobile_calculators"],
+)
+
+mediapipe_binary_graph(
+    name = "mobile_gpu_1stage_binary_graph",
+    graph = "object_occlusion_tracking_1stage.pbtxt",
+    output_name = "mobile_gpu_1stage_binary_graph.binarypb",
+    visibility = ["//visibility:public"],
+    deps = [":mobile_calculators_1stage"],
+)
diff --git a/mediapipe/graphs/object_detection_3d/calculators/BUILD b/mediapipe/graphs/object_detection_3d/calculators/BUILD
new file mode 100644
index 0000000..8f80312
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/BUILD
@@ -0,0 +1,113 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_proto_library(
+    name = "gl_animation_overlay_calculator_proto",
+    srcs = ["gl_animation_overlay_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "annotations_to_model_matrices_calculator_proto",
+    srcs = ["annotations_to_model_matrices_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "model_matrix_proto",
+    srcs = ["model_matrix.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "annotations_to_render_data_calculator_proto",
+    srcs = ["annotations_to_render_data_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_proto",
+        "//mediapipe/util:color_proto",
+    ],
+)
+
+cc_library(
+    name = "gl_animation_overlay_calculator",
+    srcs = ["gl_animation_overlay_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":gl_animation_overlay_calculator_cc_proto",
+        ":model_matrix_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/gpu:gl_calculator_helper",
+        "//mediapipe/gpu:shader_util",
+        "//mediapipe/modules/objectron/calculators:camera_parameters_cc_proto",
+        "//mediapipe/util/android:asset_manager_util",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "annotations_to_model_matrices_calculator",
+    srcs = ["annotations_to_model_matrices_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotations_to_model_matrices_calculator_cc_proto",
+        ":model_matrix_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_options_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/modules/objectron/calculators:annotation_cc_proto",
+        "//mediapipe/modules/objectron/calculators:box",
+        "//mediapipe/util:color_cc_proto",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings",
+        "@eigen_archive//:eigen3",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "annotations_to_render_data_calculator",
+    srcs = ["annotations_to_render_data_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotations_to_render_data_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_options_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/modules/objectron/calculators:annotation_cc_proto",
+        "//mediapipe/util:color_cc_proto",
+        "//mediapipe/util:render_data_cc_proto",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings",
+    ],
+    alwayslink = 1,
+)
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.cc b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.cc
new file mode 100644
index 0000000..183f6fc
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.cc
@@ -0,0 +1,215 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <memory>
+
+#include "Eigen/Core"
+#include "Eigen/Dense"
+#include "Eigen/Geometry"
+#include "absl/memory/memory.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/str_join.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_options.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.pb.h"
+#include "mediapipe/graphs/object_detection_3d/calculators/model_matrix.pb.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/box.h"
+#include "mediapipe/util/color.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kAnnotationTag[] = "ANNOTATIONS";
+constexpr char kModelMatricesTag[] = "MODEL_MATRICES";
+
+using Matrix3fRM = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
+using Matrix4fRM = Eigen::Matrix<float, 4, 4, Eigen::RowMajor>;
+
+}  // namespace
+
+// Converts the box prediction from Objectron Model to the Model matrices
+// to be rendered.
+//
+// Input:
+//  ANNOTATIONS - Frame annotations with lifted 3D points, the points are in
+//     Objectron coordinate system.
+// Output:
+//  MODEL_MATRICES - Result ModelMatrices, in OpenGL coordinate system.
+//
+// Usage example:
+// node {
+//  calculator: "AnnotationsToModelMatricesCalculator"
+//  input_stream: "ANNOTATIONS:objects"
+//  output_stream: "MODEL_MATRICES:model_matrices"
+//}
+
+class AnnotationsToModelMatricesCalculator : public CalculatorBase {
+ public:
+  AnnotationsToModelMatricesCalculator() {}
+  ~AnnotationsToModelMatricesCalculator() override {}
+  AnnotationsToModelMatricesCalculator(
+      const AnnotationsToModelMatricesCalculator&) = delete;
+  AnnotationsToModelMatricesCalculator& operator=(
+      const AnnotationsToModelMatricesCalculator&) = delete;
+
+  static absl::Status GetContract(CalculatorContract* cc);
+
+  absl::Status Open(CalculatorContext* cc) override;
+
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  absl::Status GetModelMatricesForAnnotations(
+      const FrameAnnotation& annotations,
+      TimedModelMatrixProtoList* model_matrix_list);
+
+  AnnotationsToModelMatricesCalculatorOptions options_;
+  Eigen::Vector3f model_scale_;
+  Matrix4fRM model_transformation_;
+};
+REGISTER_CALCULATOR(AnnotationsToModelMatricesCalculator);
+
+absl::Status AnnotationsToModelMatricesCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(cc->Inputs().HasTag(kAnnotationTag)) << "No input stream found.";
+  if (cc->Inputs().HasTag(kAnnotationTag)) {
+    cc->Inputs().Tag(kAnnotationTag).Set<FrameAnnotation>();
+  }
+
+  if (cc->Outputs().HasTag(kModelMatricesTag)) {
+    cc->Outputs().Tag(kModelMatricesTag).Set<TimedModelMatrixProtoList>();
+  }
+
+  if (cc->InputSidePackets().HasTag("MODEL_SCALE")) {
+    cc->InputSidePackets().Tag("MODEL_SCALE").Set<float[]>();
+  }
+
+  if (cc->InputSidePackets().HasTag("MODEL_TRANSFORMATION")) {
+    cc->InputSidePackets().Tag("MODEL_TRANSFORMATION").Set<float[]>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status AnnotationsToModelMatricesCalculator::Open(CalculatorContext* cc) {
+  RET_CHECK(cc->Inputs().HasTag(kAnnotationTag));
+
+  cc->SetOffset(TimestampDiff(0));
+  options_ = cc->Options<AnnotationsToModelMatricesCalculatorOptions>();
+
+  if (cc->InputSidePackets().HasTag("MODEL_SCALE")) {
+    model_scale_ = Eigen::Map<const Eigen::Vector3f>(
+        cc->InputSidePackets().Tag("MODEL_SCALE").Get<float[]>());
+  } else if (options_.model_scale_size() == 3) {
+    model_scale_ =
+        Eigen::Map<const Eigen::Vector3f>(options_.model_scale().data());
+  } else {
+    model_scale_.setOnes();
+  }
+
+  if (cc->InputSidePackets().HasTag("MODEL_TRANSFORMATION")) {
+    model_transformation_ = Eigen::Map<const Matrix4fRM>(
+        cc->InputSidePackets().Tag("MODEL_TRANSFORMATION").Get<float[]>());
+  } else if (options_.model_transformation_size() == 16) {
+    model_transformation_ =
+        Eigen::Map<const Matrix4fRM>(options_.model_transformation().data());
+  } else {
+    model_transformation_.setIdentity();
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status AnnotationsToModelMatricesCalculator::Process(
+    CalculatorContext* cc) {
+  auto model_matrices = std::make_unique<TimedModelMatrixProtoList>();
+
+  const FrameAnnotation& annotations =
+      cc->Inputs().Tag(kAnnotationTag).Get<FrameAnnotation>();
+
+  if (!GetModelMatricesForAnnotations(annotations, model_matrices.get()).ok()) {
+    return absl::InvalidArgumentError("Error in GetModelMatricesForBoxes");
+  }
+  cc->Outputs()
+      .Tag(kModelMatricesTag)
+      .Add(model_matrices.release(), cc->InputTimestamp());
+
+  return absl::OkStatus();
+}
+
+absl::Status
+AnnotationsToModelMatricesCalculator::GetModelMatricesForAnnotations(
+    const FrameAnnotation& annotations,
+    TimedModelMatrixProtoList* model_matrix_list) {
+  if (model_matrix_list == nullptr) {
+    return absl::InvalidArgumentError("model_matrix_list is nullptr");
+  }
+  model_matrix_list->clear_model_matrix();
+
+  for (const auto& object : annotations.annotations()) {
+    TimedModelMatrixProto* model_matrix = model_matrix_list->add_model_matrix();
+    model_matrix->set_id(object.object_id());
+
+    // Get object rotation, translation and scale.
+    const auto object_rotation =
+        Eigen::Map<const Matrix3fRM>(object.rotation().data());
+    const auto object_translation =
+        Eigen::Map<const Eigen::Vector3f>(object.translation().data());
+    const auto object_scale =
+        Eigen::Map<const Eigen::Vector3f>(object.scale().data());
+
+    // Compose object transformation matrix.
+    Matrix4fRM object_transformation;
+    object_transformation.setIdentity();
+    object_transformation.topLeftCorner<3, 3>() = object_rotation;
+    object_transformation.topRightCorner<3, 1>() = object_translation;
+
+    Matrix4fRM model_view;
+    Matrix4fRM objectron_model;
+    // The reference view is
+    //
+    // ref <<  0.,  0.,  1.,  0.,
+    //        -1.,  0., 0.,  0.,
+    //         0.,  -1.,  0.,  0.,
+    //         0.,  0.,  0.,  1.;
+    // We have objectron_model * model = model_view, to get objectron_model:
+    // objectron_model = model_view * model^-1
+    // clang-format off
+    objectron_model << 1.0, 0.0, 0.0, 0.0,
+                       0.0, -1., 0.0, 0.0,
+                       0.0, 0.0, 1.0, 0.0,
+                       0.0, 0.0, 0.0, 1.0;
+    // clang-format on
+
+    // Re-scale the CAD model to the scale of the estimated bounding box.
+    const Eigen::Vector3f scale = model_scale_.cwiseProduct(object_scale);
+    const Matrix4fRM model =
+        model_transformation_.array().colwise() * scale.homogeneous().array();
+
+    // Finally compute the model_view matrix.
+    model_view = objectron_model * object_transformation * model;
+
+    for (int i = 0; i < model_view.rows(); ++i) {
+      for (int j = 0; j < model_view.cols(); ++j) {
+        model_matrix->add_matrix_entries(model_view(i, j));
+      }
+    }
+  }
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.proto b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.proto
new file mode 100644
index 0000000..c0159d4
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.proto
@@ -0,0 +1,33 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message AnnotationsToModelMatricesCalculatorOptions {
+  extend CalculatorOptions {
+    optional AnnotationsToModelMatricesCalculatorOptions ext = 290166283;
+  }
+
+  // Vector of size 3 indicating the scale vector [x, y, z]. We will re-scale
+  // the model size with this vector. (Defaults to [1., 1., 1.])
+  repeated float model_scale = 1;
+
+  // 4x4 Row major matrix denoting the transformation from the model to the
+  // Deep Pursuit 3D coordinate system (where front is +z, and up is +y).
+  repeated float model_transformation = 2;
+}
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.cc b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.cc
new file mode 100644
index 0000000..65bff77
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.cc
@@ -0,0 +1,271 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "absl/memory/memory.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/str_join.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_options.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.pb.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/util/color.pb.h"
+#include "mediapipe/util/render_data.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kAnnotationTag[] = "ANNOTATIONS";
+constexpr char kRenderDataTag[] = "RENDER_DATA";
+constexpr char kKeypointLabel[] = "KEYPOINT";
+constexpr int kMaxLandmarkThickness = 18;
+
+inline void SetColor(RenderAnnotation* annotation, const Color& color) {
+  annotation->mutable_color()->set_r(color.r());
+  annotation->mutable_color()->set_g(color.g());
+  annotation->mutable_color()->set_b(color.b());
+}
+
+// Remap x from range [lo hi] to range [0 1] then multiply by scale.
+inline float Remap(float x, float lo, float hi, float scale) {
+  return (x - lo) / (hi - lo + 1e-6) * scale;
+}
+
+inline void GetMinMaxZ(const FrameAnnotation& annotations, float* z_min,
+                       float* z_max) {
+  *z_min = std::numeric_limits<float>::max();
+  *z_max = std::numeric_limits<float>::min();
+  // Use a global depth scale for all the objects in the scene
+  for (const auto& object : annotations.annotations()) {
+    for (const auto& keypoint : object.keypoints()) {
+      *z_min = std::min(keypoint.point_2d().depth(), *z_min);
+      *z_max = std::max(keypoint.point_2d().depth(), *z_max);
+    }
+  }
+}
+
+void SetColorSizeValueFromZ(float z, float z_min, float z_max,
+                            RenderAnnotation* render_annotation) {
+  const int color_value = 255 - static_cast<int>(Remap(z, z_min, z_max, 255));
+  ::mediapipe::Color color;
+  color.set_r(color_value);
+  color.set_g(color_value);
+  color.set_b(color_value);
+  SetColor(render_annotation, color);
+  const int thickness = static_cast<int>((1.f - Remap(z, z_min, z_max, 1)) *
+                                         kMaxLandmarkThickness);
+  render_annotation->set_thickness(thickness);
+}
+
+}  // namespace
+
+// A calculator that converts FrameAnnotation proto to RenderData proto for
+// visualization. The input should be the FrameAnnotation proto buffer. It is
+// also possible to specify the connections between landmarks.
+//
+// Example config:
+// node {
+//   calculator: "AnnotationsToRenderDataCalculator"
+//   input_stream: "ANNOTATIONS:annotations"
+//   output_stream: "RENDER_DATA:render_data"
+//   options {
+//     [AnnotationsToRenderDataCalculator.ext] {
+//       landmark_connections: [0, 1, 1, 2]
+//       landmark_color { r: 0 g: 255 b: 0 }
+//       connection_color { r: 0 g: 255 b: 0 }
+//       thickness: 4.0
+//     }
+//   }
+// }
+class AnnotationsToRenderDataCalculator : public CalculatorBase {
+ public:
+  AnnotationsToRenderDataCalculator() {}
+  ~AnnotationsToRenderDataCalculator() override {}
+  AnnotationsToRenderDataCalculator(const AnnotationsToRenderDataCalculator&) =
+      delete;
+  AnnotationsToRenderDataCalculator& operator=(
+      const AnnotationsToRenderDataCalculator&) = delete;
+
+  static absl::Status GetContract(CalculatorContract* cc);
+
+  absl::Status Open(CalculatorContext* cc) override;
+
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  static void SetRenderAnnotationColorThickness(
+      const AnnotationsToRenderDataCalculatorOptions& options,
+      RenderAnnotation* render_annotation);
+  static RenderAnnotation* AddPointRenderData(
+      const AnnotationsToRenderDataCalculatorOptions& options,
+      RenderData* render_data);
+
+  // Add a command to draw a line in the rendering queue. The line is drawn from
+  // (start_x, start_y) to (end_x, end_y). The input x,y can either be in pixel
+  // or normalized coordinate [0, 1] as indicated by the normalized flag.
+  static void AddConnectionToRenderData(
+      float start_x, float start_y, float end_x, float end_y,
+      const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
+      RenderData* render_data);
+
+  // Same as above function. Instead of using color data to render the line, it
+  // re-colors the line according to the two depth value. gray_val1 is the color
+  // of the starting point and gray_val2 is the color of the ending point. The
+  // line is colored using gradient color from gray_val1 to gray_val2. The
+  // gray_val ranges from [0 to 255] for black to white.
+  static void AddConnectionToRenderData(
+      float start_x, float start_y, float end_x, float end_y,
+      const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
+      int gray_val1, int gray_val2, RenderData* render_data);
+
+  AnnotationsToRenderDataCalculatorOptions options_;
+};
+REGISTER_CALCULATOR(AnnotationsToRenderDataCalculator);
+
+absl::Status AnnotationsToRenderDataCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(cc->Inputs().HasTag(kAnnotationTag)) << "No input stream found.";
+  if (cc->Inputs().HasTag(kAnnotationTag)) {
+    cc->Inputs().Tag(kAnnotationTag).Set<FrameAnnotation>();
+  }
+  cc->Outputs().Tag(kRenderDataTag).Set<RenderData>();
+
+  return absl::OkStatus();
+}
+
+absl::Status AnnotationsToRenderDataCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  options_ = cc->Options<AnnotationsToRenderDataCalculatorOptions>();
+
+  return absl::OkStatus();
+}
+
+absl::Status AnnotationsToRenderDataCalculator::Process(CalculatorContext* cc) {
+  auto render_data = absl::make_unique<RenderData>();
+  bool visualize_depth = options_.visualize_landmark_depth();
+  float z_min = 0.f;
+  float z_max = 0.f;
+
+  if (cc->Inputs().HasTag(kAnnotationTag)) {
+    const auto& annotations =
+        cc->Inputs().Tag(kAnnotationTag).Get<FrameAnnotation>();
+    RET_CHECK_EQ(options_.landmark_connections_size() % 2, 0)
+        << "Number of entries in landmark connections must be a multiple of 2";
+
+    if (visualize_depth) {
+      GetMinMaxZ(annotations, &z_min, &z_max);
+      // Only change rendering if there are actually z values other than 0.
+      visualize_depth &= ((z_max - z_min) > 1e-3);
+    }
+
+    for (const auto& object : annotations.annotations()) {
+      for (const auto& keypoint : object.keypoints()) {
+        auto* keypoint_data_render =
+            AddPointRenderData(options_, render_data.get());
+        auto* point = keypoint_data_render->mutable_point();
+        if (visualize_depth) {
+          SetColorSizeValueFromZ(keypoint.point_2d().depth(), z_min, z_max,
+                                 keypoint_data_render);
+        }
+
+        point->set_normalized(true);
+        point->set_x(keypoint.point_2d().x());
+        point->set_y(keypoint.point_2d().y());
+      }
+
+      // Add edges
+      for (int i = 0; i < options_.landmark_connections_size(); i += 2) {
+        const auto& ld0 =
+            object.keypoints(options_.landmark_connections(i)).point_2d();
+        const auto& ld1 =
+            object.keypoints(options_.landmark_connections(i + 1)).point_2d();
+        const bool normalized = true;
+
+        if (visualize_depth) {
+          const int gray_val1 =
+              255 - static_cast<int>(Remap(ld0.depth(), z_min, z_max, 255));
+          const int gray_val2 =
+              255 - static_cast<int>(Remap(ld1.depth(), z_min, z_max, 255));
+          AddConnectionToRenderData(ld0.x(), ld0.y(), ld1.x(), ld1.y(),
+                                    options_, normalized, gray_val1, gray_val2,
+                                    render_data.get());
+        } else {
+          AddConnectionToRenderData(ld0.x(), ld0.y(), ld1.x(), ld1.y(),
+                                    options_, normalized, render_data.get());
+        }
+      }
+    }
+  }
+
+  cc->Outputs()
+      .Tag(kRenderDataTag)
+      .Add(render_data.release(), cc->InputTimestamp());
+
+  return absl::OkStatus();
+}
+
+void AnnotationsToRenderDataCalculator::AddConnectionToRenderData(
+    float start_x, float start_y, float end_x, float end_y,
+    const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
+    int gray_val1, int gray_val2, RenderData* render_data) {
+  auto* connection_annotation = render_data->add_render_annotations();
+  RenderAnnotation::GradientLine* line =
+      connection_annotation->mutable_gradient_line();
+  line->set_x_start(start_x);
+  line->set_y_start(start_y);
+  line->set_x_end(end_x);
+  line->set_y_end(end_y);
+  line->set_normalized(normalized);
+  line->mutable_color1()->set_r(gray_val1);
+  line->mutable_color1()->set_g(gray_val1);
+  line->mutable_color1()->set_b(gray_val1);
+  line->mutable_color2()->set_r(gray_val2);
+  line->mutable_color2()->set_g(gray_val2);
+  line->mutable_color2()->set_b(gray_val2);
+  connection_annotation->set_thickness(options.thickness());
+}
+
+void AnnotationsToRenderDataCalculator::AddConnectionToRenderData(
+    float start_x, float start_y, float end_x, float end_y,
+    const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
+    RenderData* render_data) {
+  auto* connection_annotation = render_data->add_render_annotations();
+  RenderAnnotation::Line* line = connection_annotation->mutable_line();
+  line->set_x_start(start_x);
+  line->set_y_start(start_y);
+  line->set_x_end(end_x);
+  line->set_y_end(end_y);
+  line->set_normalized(normalized);
+  SetColor(connection_annotation, options.connection_color());
+  connection_annotation->set_thickness(options.thickness());
+}
+
+RenderAnnotation* AnnotationsToRenderDataCalculator::AddPointRenderData(
+    const AnnotationsToRenderDataCalculatorOptions& options,
+    RenderData* render_data) {
+  auto* landmark_data_annotation = render_data->add_render_annotations();
+  landmark_data_annotation->set_scene_tag(kKeypointLabel);
+  SetRenderAnnotationColorThickness(options, landmark_data_annotation);
+  return landmark_data_annotation;
+}
+
+void AnnotationsToRenderDataCalculator::SetRenderAnnotationColorThickness(
+    const AnnotationsToRenderDataCalculatorOptions& options,
+    RenderAnnotation* render_annotation) {
+  SetColor(render_annotation, options.landmark_color());
+  render_annotation->set_thickness(options.thickness());
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.proto b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.proto
new file mode 100644
index 0000000..1e04d95
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.proto
@@ -0,0 +1,43 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+import "mediapipe/util/color.proto";
+
+message AnnotationsToRenderDataCalculatorOptions {
+  extend CalculatorOptions {
+    optional AnnotationsToRenderDataCalculatorOptions ext = 267644238;
+  }
+
+  // Specifies the landmarks to be connected in the drawing. For example, the
+  // landmark_connections value of [0, 1, 1, 2] specifies two connections: one
+  // that connects landmarks with index 0 and 1, and another that connects
+  // landmarks with index 1 and 2.
+  repeated int32 landmark_connections = 1;
+
+  // Color of the landmarks.
+  optional Color landmark_color = 2;
+  // Color of the connections.
+  optional Color connection_color = 3;
+
+  // Thickness of the drawing of landmarks and connections.
+  optional double thickness = 4 [default = 1.0];
+
+  // Change color and size of rendered landmarks based on its z value.
+  optional bool visualize_landmark_depth = 5 [default = true];
+}
diff --git a/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc b/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc
new file mode 100644
index 0000000..9bc43ba
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc
@@ -0,0 +1,947 @@
+// Copyright 2020 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#if defined(__ANDROID__)
+#include "mediapipe/util/android/asset_manager_util.h"
+#else
+#include <fstream>
+#include <iostream>
+#endif
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/gpu/gl_calculator_helper.h"
+#include "mediapipe/gpu/shader_util.h"
+#include "mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.pb.h"
+#include "mediapipe/graphs/object_detection_3d/calculators/model_matrix.pb.h"
+#include "mediapipe/modules/objectron/calculators/camera_parameters.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+#if defined(GL_DEBUG)
+#define GLCHECK(command) \
+  command;               \
+  if (int err = glGetError()) LOG(ERROR) << "GL error detected: " << err;
+#else
+#define GLCHECK(command) command
+#endif
+
+// For ease of use, we prefer ImageFrame on Android and GpuBuffer otherwise.
+#if defined(__ANDROID__)
+typedef ImageFrame AssetTextureFormat;
+#else
+typedef GpuBuffer AssetTextureFormat;
+#endif
+
+enum { ATTRIB_VERTEX, ATTRIB_TEXTURE_POSITION, ATTRIB_NORMAL, NUM_ATTRIBUTES };
+static const int kNumMatrixEntries = 16;
+
+// Hard-coded MVP Matrix for testing.
+static const float kModelMatrix[] = {0.83704215,  -0.36174262, 0.41049102, 0.0,
+                                     0.06146407,  0.8076706,   0.5864218,  0.0,
+                                     -0.54367524, -0.4656292,  0.69828844, 0.0,
+                                     0.0,         0.0,         -98.64117,  1.0};
+
+// Loads a texture from an input side packet, and streams in an animation file
+// from a filename given in another input side packet, and renders the animation
+// over the screen according to the input timestamp and desired animation FPS.
+//
+// Inputs:
+//   VIDEO (GpuBuffer, optional):
+//     If provided, the input buffer will be assumed to be unique, and will be
+//     consumed by this calculator and rendered to directly.  The output video
+//     buffer will then be the released reference to the input video buffer.
+//   MODEL_MATRICES (TimedModelMatrixProtoList, optional):
+//     If provided, will set the model matrices for the objects to be rendered
+//     during future rendering calls.
+//   TEXTURE (ImageFrame on Android / GpuBuffer on iOS, semi-optional):
+//     Texture to use with animation file. Texture is REQUIRED to be passed into
+//     the calculator, but can be passed in as a Side Packet OR Input Stream.
+//
+// Input side packets:
+//   TEXTURE (ImageFrame on Android / GpuBuffer on iOS, semi-optional):
+//     Texture to use with animation file. Texture is REQUIRED to be passed into
+//     the calculator, but can be passed in as a Side Packet OR Input Stream.
+//   ANIMATION_ASSET (String, required):
+//     Path of animation file to load and render. The file format expects an
+//     arbitrary number of animation frames, concatenated directly together,
+//     with each animation frame looking like:
+//     HEADER
+//     VERTICES
+//     TEXTURE_COORDS
+//     INDICES
+//     The header consists of 3 int32 lengths, the sizes of the vertex data,
+//     the texcoord data, and the index data, respectively. Let us call those
+//     N1, N2, and N3. Then we expect N1 float32's for vertex information
+//     (x1,y1,z1,x2,y2,z2,etc.), followed by N2 float32's for texcoord
+//     information (u1,v1,u2,v2,u3,v3,etc.), followed by N3 shorts/int16's
+//     for triangle indices (a1,b1,c1,a2,b2,c2,etc.).
+//   CAMERA_PARAMETERS_PROTO_STRING (String, optional):
+//     Serialized proto std::string of CameraParametersProto. We need this to
+//     get the right aspect ratio and field of view.
+// Options:
+//   aspect_ratio: the ratio between the rendered image width and height.
+//     It will be ignored if CAMERA_PARAMETERS_PROTO_STRING input side packet
+//     is provided.
+//   vertical_fov_degrees: vertical field of view in degrees.
+//     It will be ignored if CAMERA_PARAMETERS_PROTO_STRING input side packet
+//     is provided.
+//   z_clipping_plane_near: near plane value for z-clipping.
+//   z_clipping_plane_far: far plane value for z-clipping.
+//   animation_speed_fps: speed at which to cycle through animation frames (in
+//     frames per second).
+//
+// Outputs:
+//   OUTPUT, or index 0 (GpuBuffer):
+//     Frames filled with the given texture.
+
+// Simple helper-struct for containing the parsed geometry data from a 3D
+// animation frame for rendering.
+struct TriangleMesh {
+  int index_count = 0;  // Needed for glDrawElements rendering call
+  std::unique_ptr<float[]> normals = nullptr;
+  std::unique_ptr<float[]> vertices = nullptr;
+  std::unique_ptr<float[]> texture_coords = nullptr;
+  std::unique_ptr<int16[]> triangle_indices = nullptr;
+};
+
+typedef std::unique_ptr<float[]> ModelMatrix;
+
+}  // namespace
+
+class GlAnimationOverlayCalculator : public CalculatorBase {
+ public:
+  GlAnimationOverlayCalculator() {}
+  ~GlAnimationOverlayCalculator();
+
+  static absl::Status GetContract(CalculatorContract *cc);
+
+  absl::Status Open(CalculatorContext *cc) override;
+  absl::Status Process(CalculatorContext *cc) override;
+
+ private:
+  bool has_video_stream_ = false;
+  bool has_model_matrix_stream_ = false;
+  bool has_mask_model_matrix_stream_ = false;
+  bool has_occlusion_mask_ = false;
+
+  GlCalculatorHelper helper_;
+  bool initialized_ = false;
+  GlTexture texture_;
+  GlTexture mask_texture_;
+
+  GLuint renderbuffer_ = 0;
+  bool depth_buffer_created_ = false;
+
+  GLuint program_ = 0;
+  GLint texture_uniform_ = -1;
+  GLint perspective_matrix_uniform_ = -1;
+  GLint model_matrix_uniform_ = -1;
+
+  std::vector<TriangleMesh> triangle_meshes_;
+  std::vector<TriangleMesh> mask_meshes_;
+  Timestamp animation_start_time_;
+  int frame_count_ = 0;
+  float animation_speed_fps_;
+
+  std::vector<ModelMatrix> current_model_matrices_;
+  std::vector<ModelMatrix> current_mask_model_matrices_;
+
+  // Perspective matrix for rendering, to be applied to all model matrices
+  // prior to passing through to the shader as a MVP matrix.  Initialized during
+  // first image packet read.
+  float perspective_matrix_[kNumMatrixEntries];
+
+  void ComputeAspectRatioAndFovFromCameraParameters(
+      const CameraParametersProto &camera_parameters, float *aspect_ratio,
+      float *vertical_fov_degrees);
+
+  int GetAnimationFrameIndex(Timestamp timestamp);
+  absl::Status GlSetup();
+  absl::Status GlBind(const TriangleMesh &triangle_mesh,
+                      const GlTexture &texture);
+  absl::Status GlRender(const TriangleMesh &triangle_mesh,
+                        const float *model_matrix);
+  void InitializePerspectiveMatrix(float aspect_ratio,
+                                   float vertical_fov_degrees, float z_near,
+                                   float z_far);
+  void LoadModelMatrices(const TimedModelMatrixProtoList &model_matrices,
+                         std::vector<ModelMatrix> *current_model_matrices);
+  void CalculateTriangleMeshNormals(int normals_len,
+                                    TriangleMesh *triangle_mesh);
+  void Normalize3f(float input[3]);
+
+#if !defined(__ANDROID__)
+  // Asset loading routine for all non-Android platforms.
+  bool LoadAnimation(const std::string &filename);
+#else
+  // Asset loading for all Android platforms.
+  bool LoadAnimationAndroid(const std::string &filename,
+                            std::vector<TriangleMesh> *mesh);
+  bool ReadBytesFromAsset(AAsset *asset, void *buffer, int num_bytes_to_read);
+#endif
+};
+REGISTER_CALCULATOR(GlAnimationOverlayCalculator);
+
+// static
+absl::Status GlAnimationOverlayCalculator::GetContract(CalculatorContract *cc) {
+  MP_RETURN_IF_ERROR(
+      GlCalculatorHelper::SetupInputSidePackets(&(cc->InputSidePackets())));
+  if (cc->Inputs().HasTag("VIDEO")) {
+    // Currently used only for size and timestamp.
+    cc->Inputs().Tag("VIDEO").Set<GpuBuffer>();
+  }
+  TagOrIndex(&(cc->Outputs()), "OUTPUT", 0).Set<GpuBuffer>();
+
+  if (cc->Inputs().HasTag("MODEL_MATRICES")) {
+    cc->Inputs().Tag("MODEL_MATRICES").Set<TimedModelMatrixProtoList>();
+  }
+  if (cc->Inputs().HasTag("MASK_MODEL_MATRICES")) {
+    cc->Inputs().Tag("MASK_MODEL_MATRICES").Set<TimedModelMatrixProtoList>();
+  }
+
+  // Must have texture as Input Stream or Side Packet
+  if (cc->InputSidePackets().HasTag("TEXTURE")) {
+    cc->InputSidePackets().Tag("TEXTURE").Set<AssetTextureFormat>();
+  } else {
+    cc->Inputs().Tag("TEXTURE").Set<AssetTextureFormat>();
+  }
+
+  cc->InputSidePackets().Tag("ANIMATION_ASSET").Set<std::string>();
+  if (cc->InputSidePackets().HasTag("CAMERA_PARAMETERS_PROTO_STRING")) {
+    cc->InputSidePackets()
+        .Tag("CAMERA_PARAMETERS_PROTO_STRING")
+        .Set<std::string>();
+  }
+
+  if (cc->InputSidePackets().HasTag("MASK_TEXTURE")) {
+    cc->InputSidePackets().Tag("MASK_TEXTURE").Set<AssetTextureFormat>();
+  }
+  if (cc->InputSidePackets().HasTag("MASK_ASSET")) {
+    cc->InputSidePackets().Tag("MASK_ASSET").Set<std::string>();
+  }
+
+  return absl::OkStatus();
+}
+
+void GlAnimationOverlayCalculator::CalculateTriangleMeshNormals(
+    int normals_len, TriangleMesh *triangle_mesh) {
+  // Set triangle_mesh normals for shader usage
+  triangle_mesh->normals.reset(new float[normals_len]);
+  // Used for storing the vertex normals prior to averaging
+  std::vector<float> vertex_normals_sum(normals_len, 0.0f);
+  // Compute every triangle surface normal and store them for averaging
+  for (int idx = 0; idx < triangle_mesh->index_count; idx += 3) {
+    int v_idx[3];
+    v_idx[0] = triangle_mesh->triangle_indices.get()[idx];
+    v_idx[1] = triangle_mesh->triangle_indices.get()[idx + 1];
+    v_idx[2] = triangle_mesh->triangle_indices.get()[idx + 2];
+    // (V1) vertex X,Y,Z indices in triangle_mesh.vertices
+    const float v1x = triangle_mesh->vertices[v_idx[0] * 3];
+    const float v1y = triangle_mesh->vertices[v_idx[0] * 3 + 1];
+    const float v1z = triangle_mesh->vertices[v_idx[0] * 3 + 2];
+    // (V2) vertex X,Y,Z indices in triangle_mesh.vertices
+    const float v2x = triangle_mesh->vertices[v_idx[1] * 3];
+    const float v2y = triangle_mesh->vertices[v_idx[1] * 3 + 1];
+    const float v2z = triangle_mesh->vertices[v_idx[1] * 3 + 2];
+    // (V3) vertex X,Y,Z indices in triangle_mesh.vertices
+    const float v3x = triangle_mesh->vertices[v_idx[2] * 3];
+    const float v3y = triangle_mesh->vertices[v_idx[2] * 3 + 1];
+    const float v3z = triangle_mesh->vertices[v_idx[2] * 3 + 2];
+    // Calculate normals from vertices
+    // V2 - V1
+    const float ax = v2x - v1x;
+    const float ay = v2y - v1y;
+    const float az = v2z - v1z;
+    // V3 - V1
+    const float bx = v3x - v1x;
+    const float by = v3y - v1y;
+    const float bz = v3z - v1z;
+    // Calculate cross product
+    const float normal_x = ay * bz - az * by;
+    const float normal_y = az * bx - ax * bz;
+    const float normal_z = ax * by - ay * bx;
+    // The normals calculated above must be normalized if we wish to prevent
+    // triangles with a larger surface area from dominating the normal
+    // calculations, however, none of our current models require this
+    // normalization.
+
+    // Add connected normal to each associated vertex
+    // It is also necessary to increment each vertex denominator for averaging
+    for (int i = 0; i < 3; i++) {
+      vertex_normals_sum[v_idx[i] * 3] += normal_x;
+      vertex_normals_sum[v_idx[i] * 3 + 1] += normal_y;
+      vertex_normals_sum[v_idx[i] * 3 + 2] += normal_z;
+    }
+  }
+
+  // Combine all triangle normals connected to each vertex by adding the X,Y,Z
+  // value of each adjacent triangle surface normal to every vertex and then
+  // averaging the combined value.
+  for (int idx = 0; idx < normals_len; idx += 3) {
+    float normal[3];
+    normal[0] = vertex_normals_sum[idx];
+    normal[1] = vertex_normals_sum[idx + 1];
+    normal[2] = vertex_normals_sum[idx + 2];
+    Normalize3f(normal);
+    triangle_mesh->normals.get()[idx] = normal[0];
+    triangle_mesh->normals.get()[idx + 1] = normal[1];
+    triangle_mesh->normals.get()[idx + 2] = normal[2];
+  }
+}
+
+void GlAnimationOverlayCalculator::Normalize3f(float input[3]) {
+  float product = 0.0;
+  product += input[0] * input[0];
+  product += input[1] * input[1];
+  product += input[2] * input[2];
+  float magnitude = sqrt(product);
+  input[0] /= magnitude;
+  input[1] /= magnitude;
+  input[2] /= magnitude;
+}
+
+// Helper function for initializing our perspective matrix.
+void GlAnimationOverlayCalculator::InitializePerspectiveMatrix(
+    float aspect_ratio, float fov_degrees, float z_near, float z_far) {
+  // Standard perspective projection matrix calculations.
+  const float f = 1.0f / std::tan(fov_degrees * M_PI / 360.0f);
+  for (int i = 0; i < kNumMatrixEntries; i++) {
+    perspective_matrix_[i] = 0;
+  }
+  const float denom = 1.0f / (z_near - z_far);
+  perspective_matrix_[0] = f / aspect_ratio;
+  perspective_matrix_[5] = f;
+  perspective_matrix_[10] = (z_near + z_far) * denom;
+  perspective_matrix_[11] = -1.0f;
+  perspective_matrix_[14] = 2.0f * z_far * z_near * denom;
+}
+
+#if defined(__ANDROID__)
+// Helper function for reading in a specified number of bytes from an Android
+// asset.  Returns true if successfully reads in all bytes into buffer.
+bool GlAnimationOverlayCalculator::ReadBytesFromAsset(AAsset *asset,
+                                                      void *buffer,
+                                                      int num_bytes_to_read) {
+  // Most file systems use block sizes of 4KB or 8KB; ideally we'd choose a
+  // small multiple of the block size for best input streaming performance, so
+  // we go for a reasobably safe buffer size of 8KB = 8*1024 bytes.
+  static const int kMaxChunkSize = 8192;
+
+  int bytes_left = num_bytes_to_read;
+  int bytes_read = 1;  // any value > 0 here just to start looping.
+
+  // Treat as uint8_t array so we can deal in single byte arithmetic easily.
+  uint8_t *currBufferIndex = reinterpret_cast<uint8_t *>(buffer);
+  while (bytes_read > 0 && bytes_left > 0) {
+    bytes_read = AAsset_read(asset, (void *)currBufferIndex,
+                             std::min(bytes_left, kMaxChunkSize));
+    bytes_left -= bytes_read;
+    currBufferIndex += bytes_read;
+  }
+  // At least log any I/O errors encountered.
+  if (bytes_read < 0) {
+    LOG(ERROR) << "Error reading from AAsset: " << bytes_read;
+    return false;
+  }
+  if (bytes_left > 0) {
+    // Reached EOF before reading in specified number of bytes.
+    LOG(WARNING) << "Reached EOF before reading in specified number of bytes.";
+    return false;
+  }
+  return true;
+}
+
+// The below asset streaming code is Android-only, making use of the platform
+// JNI helper classes AAssetManager and AAsset.
+bool GlAnimationOverlayCalculator::LoadAnimationAndroid(
+    const std::string &filename, std::vector<TriangleMesh> *meshes) {
+  mediapipe::AssetManager *mediapipe_asset_manager =
+      Singleton<mediapipe::AssetManager>::get();
+  AAssetManager *asset_manager = mediapipe_asset_manager->GetAssetManager();
+  if (!asset_manager) {
+    LOG(ERROR) << "Failed to access Android asset manager.";
+    return false;
+  }
+
+  // New read-bytes stuff here!  First we open file for streaming.
+  AAsset *asset = AAssetManager_open(asset_manager, filename.c_str(),
+                                     AASSET_MODE_STREAMING);
+  if (!asset) {
+    LOG(ERROR) << "Failed to open animation asset: " << filename;
+    return false;
+  }
+
+  // And now, while we are able to stream in more frames, we do so.
+  frame_count_ = 0;
+  int32 lengths[3];
+  while (ReadBytesFromAsset(asset, (void *)lengths, sizeof(lengths[0]) * 3)) {
+    // About to start reading the next animation frame.  Stream it in here.
+    // Each frame stores first the object counts of its three arrays
+    // (vertices, texture coordinates, triangle indices; respectively), and
+    // then stores each of those arrays as a byte dump, in order.
+    meshes->emplace_back();
+    TriangleMesh &triangle_mesh = meshes->back();
+    // Try to read in vertices (4-byte floats)
+    triangle_mesh.vertices.reset(new float[lengths[0]]);
+    if (!ReadBytesFromAsset(asset, (void *)triangle_mesh.vertices.get(),
+                            sizeof(float) * lengths[0])) {
+      LOG(ERROR) << "Failed to read vertices for frame " << frame_count_;
+      return false;
+    }
+    // Try to read in texture coordinates (4-byte floats)
+    triangle_mesh.texture_coords.reset(new float[lengths[1]]);
+    if (!ReadBytesFromAsset(asset, (void *)triangle_mesh.texture_coords.get(),
+                            sizeof(float) * lengths[1])) {
+      LOG(ERROR) << "Failed to read tex-coords for frame " << frame_count_;
+      return false;
+    }
+    // Try to read in indices (2-byte shorts)
+    triangle_mesh.index_count = lengths[2];
+    triangle_mesh.triangle_indices.reset(new int16[lengths[2]]);
+    if (!ReadBytesFromAsset(asset, (void *)triangle_mesh.triangle_indices.get(),
+                            sizeof(int16) * lengths[2])) {
+      LOG(ERROR) << "Failed to read indices for frame " << frame_count_;
+      return false;
+    }
+
+    // Set the normals for this triangle_mesh
+    CalculateTriangleMeshNormals(lengths[0], &triangle_mesh);
+
+    frame_count_++;
+  }
+  AAsset_close(asset);
+
+  LOG(INFO) << "Finished parsing " << frame_count_ << " animation frames.";
+  if (meshes->empty()) {
+    LOG(ERROR) << "No animation frames were parsed!  Erroring out calculator.";
+    return false;
+  }
+  return true;
+}
+
+#else  // defined(__ANDROID__)
+
+bool GlAnimationOverlayCalculator::LoadAnimation(const std::string &filename) {
+  std::ifstream infile(filename.c_str(), std::ifstream::binary);
+  if (!infile) {
+    LOG(ERROR) << "Error opening asset with filename: " << filename;
+    return false;
+  }
+
+  frame_count_ = 0;
+  int32 lengths[3];
+  while (true) {
+    // See if we have more initial size counts to read in.
+    infile.read((char *)(lengths), sizeof(lengths[0]) * 3);
+    if (!infile) {
+      // No more frames to read.  Close out.
+      infile.close();
+      break;
+    }
+
+    triangle_meshes_.emplace_back();
+    TriangleMesh &triangle_mesh = triangle_meshes_.back();
+
+    // Try to read in vertices (4-byte floats).
+    triangle_mesh.vertices.reset(new float[lengths[0]]);
+    infile.read((char *)(triangle_mesh.vertices.get()),
+                sizeof(float) * lengths[0]);
+    if (!infile) {
+      LOG(ERROR) << "Failed to read vertices for frame " << frame_count_;
+      return false;
+    }
+
+    // Try to read in texture coordinates (4-byte floats)
+    triangle_mesh.texture_coords.reset(new float[lengths[1]]);
+    infile.read((char *)(triangle_mesh.texture_coords.get()),
+                sizeof(float) * lengths[1]);
+    if (!infile) {
+      LOG(ERROR) << "Failed to read texture coordinates for frame "
+                 << frame_count_;
+      return false;
+    }
+
+    // Try to read in the triangle indices (2-byte shorts)
+    triangle_mesh.index_count = lengths[2];
+    triangle_mesh.triangle_indices.reset(new int16[lengths[2]]);
+    infile.read((char *)(triangle_mesh.triangle_indices.get()),
+                sizeof(int16) * lengths[2]);
+    if (!infile) {
+      LOG(ERROR) << "Failed to read triangle indices for frame "
+                 << frame_count_;
+      return false;
+    }
+
+    // Set the normals for this triangle_mesh
+    CalculateTriangleMeshNormals(lengths[0], &triangle_mesh);
+
+    frame_count_++;
+  }
+
+  LOG(INFO) << "Finished parsing " << frame_count_ << " animation frames.";
+  if (triangle_meshes_.empty()) {
+    LOG(ERROR) << "No animation frames were parsed!  Erroring out calculator.";
+    return false;
+  }
+  return true;
+}
+
+#endif
+
+void GlAnimationOverlayCalculator::ComputeAspectRatioAndFovFromCameraParameters(
+    const CameraParametersProto &camera_parameters, float *aspect_ratio,
+    float *vertical_fov_degrees) {
+  CHECK(aspect_ratio != nullptr);
+  CHECK(vertical_fov_degrees != nullptr);
+  *aspect_ratio =
+      camera_parameters.portrait_width() / camera_parameters.portrait_height();
+  *vertical_fov_degrees =
+      std::atan(camera_parameters.portrait_height() * 0.5f) * 2 * 180 / M_PI;
+}
+
+absl::Status GlAnimationOverlayCalculator::Open(CalculatorContext *cc) {
+  cc->SetOffset(TimestampDiff(0));
+  MP_RETURN_IF_ERROR(helper_.Open(cc));
+
+  const auto &options = cc->Options<GlAnimationOverlayCalculatorOptions>();
+
+  animation_speed_fps_ = options.animation_speed_fps();
+
+  // Construct projection matrix using input side packets or option
+  float aspect_ratio;
+  float vertical_fov_degrees;
+  if (cc->InputSidePackets().HasTag("CAMERA_PARAMETERS_PROTO_STRING")) {
+    const std::string &camera_parameters_proto_string =
+        cc->InputSidePackets()
+            .Tag("CAMERA_PARAMETERS_PROTO_STRING")
+            .Get<std::string>();
+    CameraParametersProto camera_parameters_proto;
+    camera_parameters_proto.ParseFromString(camera_parameters_proto_string);
+    ComputeAspectRatioAndFovFromCameraParameters(
+        camera_parameters_proto, &aspect_ratio, &vertical_fov_degrees);
+  } else {
+    aspect_ratio = options.aspect_ratio();
+    vertical_fov_degrees = options.vertical_fov_degrees();
+  }
+
+  // when constructing projection matrix.
+  InitializePerspectiveMatrix(aspect_ratio, vertical_fov_degrees,
+                              options.z_clipping_plane_near(),
+                              options.z_clipping_plane_far());
+
+  // See what streams we have.
+  has_video_stream_ = cc->Inputs().HasTag("VIDEO");
+  has_model_matrix_stream_ = cc->Inputs().HasTag("MODEL_MATRICES");
+  has_mask_model_matrix_stream_ = cc->Inputs().HasTag("MASK_MODEL_MATRICES");
+
+  // Try to load in the animation asset in a platform-specific manner.
+  const std::string &asset_name =
+      cc->InputSidePackets().Tag("ANIMATION_ASSET").Get<std::string>();
+  bool loaded_animation = false;
+#if defined(__ANDROID__)
+  if (cc->InputSidePackets().HasTag("MASK_ASSET")) {
+    has_occlusion_mask_ = true;
+    const std::string &mask_asset_name =
+        cc->InputSidePackets().Tag("MASK_ASSET").Get<std::string>();
+    loaded_animation = LoadAnimationAndroid(mask_asset_name, &mask_meshes_);
+    if (!loaded_animation) {
+      LOG(ERROR) << "Failed to load mask asset.";
+      return absl::UnknownError("Failed to load mask asset.");
+    }
+  }
+  loaded_animation = LoadAnimationAndroid(asset_name, &triangle_meshes_);
+#else
+  loaded_animation = LoadAnimation(asset_name);
+#endif
+  if (!loaded_animation) {
+    LOG(ERROR) << "Failed to load animation asset.";
+    return absl::UnknownError("Failed to load animation asset.");
+  }
+
+  return helper_.RunInGlContext([this, &cc]() -> absl::Status {
+    if (cc->InputSidePackets().HasTag("MASK_TEXTURE")) {
+      const auto &mask_texture =
+          cc->InputSidePackets().Tag("MASK_TEXTURE").Get<AssetTextureFormat>();
+      mask_texture_ = helper_.CreateSourceTexture(mask_texture);
+    }
+
+    // Load in all static texture data if it exists
+    if (cc->InputSidePackets().HasTag("TEXTURE")) {
+      const auto &input_texture =
+          cc->InputSidePackets().Tag("TEXTURE").Get<AssetTextureFormat>();
+      texture_ = helper_.CreateSourceTexture(input_texture);
+    }
+
+    VLOG(2) << "Input texture size: " << texture_.width() << ", "
+            << texture_.height() << std::endl;
+
+    return absl::OkStatus();
+  });
+}
+
+int GlAnimationOverlayCalculator::GetAnimationFrameIndex(Timestamp timestamp) {
+  double seconds_delta = timestamp.Seconds() - animation_start_time_.Seconds();
+  int64_t frame_index =
+      static_cast<int64_t>(seconds_delta * animation_speed_fps_);
+  frame_index %= frame_count_;
+  return static_cast<int>(frame_index);
+}
+
+void GlAnimationOverlayCalculator::LoadModelMatrices(
+    const TimedModelMatrixProtoList &model_matrices,
+    std::vector<ModelMatrix> *current_model_matrices) {
+  current_model_matrices->clear();
+  for (int i = 0; i < model_matrices.model_matrix_size(); ++i) {
+    const auto &model_matrix = model_matrices.model_matrix(i);
+    CHECK(model_matrix.matrix_entries_size() == kNumMatrixEntries)
+        << "Invalid Model Matrix";
+    current_model_matrices->emplace_back();
+    ModelMatrix &new_matrix = current_model_matrices->back();
+    new_matrix.reset(new float[kNumMatrixEntries]);
+    for (int j = 0; j < kNumMatrixEntries; j++) {
+      // Model matrices streamed in using ROW-MAJOR format, but we want
+      // COLUMN-MAJOR for rendering, so we transpose here.
+      int col = j % 4;
+      int row = j / 4;
+      new_matrix[row + col * 4] = model_matrix.matrix_entries(j);
+    }
+  }
+}
+
+absl::Status GlAnimationOverlayCalculator::Process(CalculatorContext *cc) {
+  return helper_.RunInGlContext([this, &cc]() -> absl::Status {
+    if (!initialized_) {
+      MP_RETURN_IF_ERROR(GlSetup());
+      initialized_ = true;
+      animation_start_time_ = cc->InputTimestamp();
+    }
+
+    // Process model matrices, if any are being streamed in, and update our
+    // list.
+    current_model_matrices_.clear();
+    if (has_model_matrix_stream_ &&
+        !cc->Inputs().Tag("MODEL_MATRICES").IsEmpty()) {
+      const TimedModelMatrixProtoList &model_matrices =
+          cc->Inputs().Tag("MODEL_MATRICES").Get<TimedModelMatrixProtoList>();
+      LoadModelMatrices(model_matrices, &current_model_matrices_);
+    }
+
+    current_mask_model_matrices_.clear();
+    if (has_mask_model_matrix_stream_ &&
+        !cc->Inputs().Tag("MASK_MODEL_MATRICES").IsEmpty()) {
+      const TimedModelMatrixProtoList &model_matrices =
+          cc->Inputs()
+              .Tag("MASK_MODEL_MATRICES")
+              .Get<TimedModelMatrixProtoList>();
+      LoadModelMatrices(model_matrices, &current_mask_model_matrices_);
+    }
+
+    // Arbitrary default width and height for output destination texture, in the
+    // event that we don't have a valid and unique input buffer to overlay.
+    int width = 640;
+    int height = 480;
+
+    GlTexture dst;
+    std::unique_ptr<GpuBuffer> input_frame(nullptr);
+    if (has_video_stream_ && !(cc->Inputs().Tag("VIDEO").IsEmpty())) {
+      auto result = cc->Inputs().Tag("VIDEO").Value().Consume<GpuBuffer>();
+      if (result.ok()) {
+        input_frame = std::move(result).value();
+#if !MEDIAPIPE_GPU_BUFFER_USE_CV_PIXEL_BUFFER
+        input_frame->GetGlTextureBufferSharedPtr()->Reuse();
+#endif
+        width = input_frame->width();
+        height = input_frame->height();
+        dst = helper_.CreateSourceTexture(*input_frame);
+      } else {
+        LOG(ERROR) << "Unable to consume input video frame for overlay!";
+        LOG(ERROR) << "Status returned was: " << result.status();
+        dst = helper_.CreateDestinationTexture(width, height);
+      }
+    } else if (!has_video_stream_) {
+      dst = helper_.CreateDestinationTexture(width, height);
+    } else {
+      // We have an input video stream, but not for this frame. Don't render!
+      return absl::OkStatus();
+    }
+    helper_.BindFramebuffer(dst);
+
+    if (!depth_buffer_created_) {
+      // Create our private depth buffer.
+      GLCHECK(glGenRenderbuffers(1, &renderbuffer_));
+      GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, renderbuffer_));
+      GLCHECK(glRenderbufferStorage(GL_RENDERBUFFER, GL_DEPTH_COMPONENT16,
+                                    width, height));
+      GLCHECK(glFramebufferRenderbuffer(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
+                                        GL_RENDERBUFFER, renderbuffer_));
+      GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, 0));
+      depth_buffer_created_ = true;
+    }
+
+    // Re-bind our depth renderbuffer to our FBO depth attachment here.
+    GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, renderbuffer_));
+    GLCHECK(glFramebufferRenderbuffer(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
+                                      GL_RENDERBUFFER, renderbuffer_));
+    GLenum status = GLCHECK(glCheckFramebufferStatus(GL_FRAMEBUFFER));
+    if (status != GL_FRAMEBUFFER_COMPLETE) {
+      LOG(ERROR) << "Incomplete framebuffer with status: " << status;
+    }
+    GLCHECK(glClear(GL_DEPTH_BUFFER_BIT));
+
+    if (has_occlusion_mask_) {
+      glColorMask(GL_FALSE, GL_FALSE, GL_FALSE, GL_FALSE);
+      const TriangleMesh &mask_frame = mask_meshes_.front();
+      MP_RETURN_IF_ERROR(GlBind(mask_frame, mask_texture_));
+      // Draw objects using our latest model matrix stream packet.
+      for (const ModelMatrix &model_matrix : current_mask_model_matrices_) {
+        MP_RETURN_IF_ERROR(GlRender(mask_frame, model_matrix.get()));
+      }
+    }
+
+    glColorMask(GL_TRUE, GL_TRUE, GL_TRUE, GL_TRUE);
+    int frame_index = GetAnimationFrameIndex(cc->InputTimestamp());
+    const TriangleMesh &current_frame = triangle_meshes_[frame_index];
+
+    // Load dynamic texture if it exists
+    if (cc->Inputs().HasTag("TEXTURE")) {
+      const auto &input_texture =
+          cc->Inputs().Tag("TEXTURE").Get<AssetTextureFormat>();
+      texture_ = helper_.CreateSourceTexture(input_texture);
+    }
+
+    MP_RETURN_IF_ERROR(GlBind(current_frame, texture_));
+    if (has_model_matrix_stream_) {
+      // Draw objects using our latest model matrix stream packet.
+      for (const ModelMatrix &model_matrix : current_model_matrices_) {
+        MP_RETURN_IF_ERROR(GlRender(current_frame, model_matrix.get()));
+      }
+    } else {
+      // Just draw one object to a static model matrix.
+      MP_RETURN_IF_ERROR(GlRender(current_frame, kModelMatrix));
+    }
+
+    // Disable vertex attributes
+    GLCHECK(glDisableVertexAttribArray(ATTRIB_VERTEX));
+    GLCHECK(glDisableVertexAttribArray(ATTRIB_TEXTURE_POSITION));
+    GLCHECK(glDisableVertexAttribArray(ATTRIB_NORMAL));
+
+    // Disable depth test
+    GLCHECK(glDisable(GL_DEPTH_TEST));
+
+    // Unbind texture
+    GLCHECK(glActiveTexture(GL_TEXTURE1));
+    GLCHECK(glBindTexture(texture_.target(), 0));
+
+    // Unbind depth buffer
+    GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, 0));
+
+    GLCHECK(glFlush());
+
+    auto output = dst.GetFrame<GpuBuffer>();
+    dst.Release();
+    TagOrIndex(&(cc->Outputs()), "OUTPUT", 0)
+        .Add(output.release(), cc->InputTimestamp());
+    GLCHECK(glFrontFace(GL_CCW));
+    return absl::OkStatus();
+  });
+}
+
+absl::Status GlAnimationOverlayCalculator::GlSetup() {
+  // Load vertex and fragment shaders
+  const GLint attr_location[NUM_ATTRIBUTES] = {
+      ATTRIB_VERTEX,
+      ATTRIB_TEXTURE_POSITION,
+      ATTRIB_NORMAL,
+  };
+  const GLchar *attr_name[NUM_ATTRIBUTES] = {
+      "position",
+      "texture_coordinate",
+      "normal",
+  };
+
+  const GLchar *vert_src = R"(
+    // Perspective projection matrix for rendering / clipping
+    uniform mat4 perspectiveMatrix;
+
+    // Matrix defining the currently rendered object model
+    uniform mat4 modelMatrix;
+
+    // vertex position in threespace
+    attribute vec4 position;
+    attribute vec3 normal;
+
+    // texture coordinate for each vertex in normalized texture space (0..1)
+    attribute mediump vec4 texture_coordinate;
+
+    // texture coordinate for fragment shader (will be interpolated)
+    varying mediump vec2 sampleCoordinate;
+    varying mediump vec3 vNormal;
+
+    void main() {
+      sampleCoordinate = texture_coordinate.xy;
+      mat4 mvpMatrix = perspectiveMatrix * modelMatrix;
+      gl_Position = mvpMatrix * position;
+
+      // TODO: Pass in rotation submatrix with no scaling or transforms to prevent
+      // breaking vNormal in case of model matrix having non-uniform scaling
+      vec4 tmpNormal = mvpMatrix * vec4(normal, 1.0);
+      vec4 transformedZero = mvpMatrix * vec4(0.0, 0.0, 0.0, 1.0);
+      tmpNormal = tmpNormal - transformedZero;
+      vNormal = normalize(tmpNormal.xyz);
+    }
+  )";
+
+  const GLchar *frag_src = R"(
+    precision mediump float;
+
+    varying vec2 sampleCoordinate;  // texture coordinate (0..1)
+    varying vec3 vNormal;
+    uniform sampler2D texture;  // texture to shade with
+    const float kPi = 3.14159265359;
+
+    // Define ambient lighting factor that is applied to our texture in order to
+    // generate ambient lighting of the scene on the object. Range is [0.0-1.0],
+    // with the factor being proportional to the brightness of the lighting in the
+    // scene being applied to the object
+    const float kAmbientLighting = 0.75;
+
+    // Define RGB values for light source
+    const vec3 kLightColor = vec3(0.25);
+    // Exponent for directional lighting that governs diffusion of surface light
+    const float kExponent = 1.0;
+    // Define direction of lighting effect source
+    const vec3 lightDir = vec3(0.0, -1.0, -0.6);
+    // Hard-coded view direction
+    const vec3 viewDir = vec3(0.0, 0.0, -1.0);
+
+    // DirectionalLighting procedure imported from Lullaby @ https://github.com/google/lullaby
+    // Calculate and return the color (diffuse and specular together) reflected by
+    // a directional light.
+    vec3 GetDirectionalLight(vec3 pos, vec3 normal, vec3 viewDir, vec3 lightDir, vec3 lightColor, float exponent) {
+      // Intensity of the diffuse light. Saturate to keep within the 0-1 range.
+      float normal_dot_light_dir = dot(-normal, -lightDir);
+      float intensity = clamp(normal_dot_light_dir, 0.0, 1.0);
+      // Calculate the diffuse light
+      vec3 diffuse = intensity * lightColor;
+      // http://www.rorydriscoll.com/2009/01/25/energy-conservation-in-games/
+      float kEnergyConservation = (2.0 + exponent) / (2.0 * kPi);
+      vec3 reflect_dir = reflect(lightDir, -normal);
+      // Intensity of the specular light
+      float view_dot_reflect = dot(-viewDir, reflect_dir);
+      // Use an epsilon for pow because pow(x,y) is undefined if x < 0 or x == 0
+      // and y <= 0 (GLSL Spec 8.2)
+      const float kEpsilon = 1e-5;
+      intensity = kEnergyConservation * pow(clamp(view_dot_reflect, kEpsilon, 1.0),
+       exponent);
+      // Specular color:
+      vec3 specular = intensity * lightColor;
+      return diffuse + specular;
+    }
+
+    void main() {
+      // Sample the texture, retrieving an rgba pixel value
+      vec4 pixel = texture2D(texture, sampleCoordinate);
+      // If the alpha (background) value is near transparent, then discard the
+      // pixel, this allows the rendering of transparent background GIFs
+      // TODO: Adding a toggle to perform pixel alpha discarding for transparent
+      // GIFs (prevent interference with Objectron system).
+      if (pixel.a < 0.2) discard;
+
+      // Generate directional lighting effect
+      vec3 lighting = GetDirectionalLight(gl_FragCoord.xyz, vNormal, viewDir, lightDir, kLightColor, kExponent);
+      // Apply both ambient and directional lighting to our texture
+      gl_FragColor = vec4((vec3(kAmbientLighting) + lighting) * pixel.rgb, 1.0);
+    }
+  )";
+
+  // Shader program
+  GLCHECK(GlhCreateProgram(vert_src, frag_src, NUM_ATTRIBUTES,
+                           (const GLchar **)&attr_name[0], attr_location,
+                           &program_));
+  RET_CHECK(program_) << "Problem initializing the program.";
+  texture_uniform_ = GLCHECK(glGetUniformLocation(program_, "texture"));
+  perspective_matrix_uniform_ =
+      GLCHECK(glGetUniformLocation(program_, "perspectiveMatrix"));
+  model_matrix_uniform_ =
+      GLCHECK(glGetUniformLocation(program_, "modelMatrix"));
+  return absl::OkStatus();
+}
+
+absl::Status GlAnimationOverlayCalculator::GlBind(
+    const TriangleMesh &triangle_mesh, const GlTexture &texture) {
+  GLCHECK(glUseProgram(program_));
+
+  // Disable backface culling to allow occlusion effects.
+  // Some options for solid arbitrary 3D geometry rendering
+  GLCHECK(glEnable(GL_BLEND));
+  GLCHECK(glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA));
+  GLCHECK(glEnable(GL_DEPTH_TEST));
+  GLCHECK(glFrontFace(GL_CW));
+  GLCHECK(glDepthMask(GL_TRUE));
+  GLCHECK(glDepthFunc(GL_LESS));
+
+  // Clear our depth buffer before starting draw calls
+  GLCHECK(glVertexAttribPointer(ATTRIB_VERTEX, 3, GL_FLOAT, 0, 0,
+                                triangle_mesh.vertices.get()));
+  GLCHECK(glEnableVertexAttribArray(ATTRIB_VERTEX));
+  GLCHECK(glVertexAttribPointer(ATTRIB_TEXTURE_POSITION, 2, GL_FLOAT, 0, 0,
+                                triangle_mesh.texture_coords.get()));
+  GLCHECK(glEnableVertexAttribArray(ATTRIB_TEXTURE_POSITION));
+  GLCHECK(glVertexAttribPointer(ATTRIB_NORMAL, 3, GL_FLOAT, 0, 0,
+                                triangle_mesh.normals.get()));
+  GLCHECK(glEnableVertexAttribArray(ATTRIB_NORMAL));
+  GLCHECK(glActiveTexture(GL_TEXTURE1));
+  GLCHECK(glBindTexture(texture.target(), texture.name()));
+
+  // We previously bound it to GL_TEXTURE1
+  GLCHECK(glUniform1i(texture_uniform_, 1));
+
+  GLCHECK(glUniformMatrix4fv(perspective_matrix_uniform_, 1, GL_FALSE,
+                             perspective_matrix_));
+  return absl::OkStatus();
+}
+
+absl::Status GlAnimationOverlayCalculator::GlRender(
+    const TriangleMesh &triangle_mesh, const float *model_matrix) {
+  GLCHECK(glUniformMatrix4fv(model_matrix_uniform_, 1, GL_FALSE, model_matrix));
+  GLCHECK(glDrawElements(GL_TRIANGLES, triangle_mesh.index_count,
+                         GL_UNSIGNED_SHORT,
+                         triangle_mesh.triangle_indices.get()));
+  return absl::OkStatus();
+}
+
+GlAnimationOverlayCalculator::~GlAnimationOverlayCalculator() {
+  helper_.RunInGlContext([this] {
+    if (program_) {
+      GLCHECK(glDeleteProgram(program_));
+      program_ = 0;
+    }
+    if (depth_buffer_created_) {
+      GLCHECK(glDeleteRenderbuffers(1, &renderbuffer_));
+      renderbuffer_ = 0;
+    }
+    if (texture_.width() > 0) {
+      texture_.Release();
+    }
+    if (mask_texture_.width() > 0) {
+      mask_texture_.Release();
+    }
+  });
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.proto b/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.proto
new file mode 100644
index 0000000..4966f0a
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.proto
@@ -0,0 +1,41 @@
+// Copyright 2019 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message GlAnimationOverlayCalculatorOptions {
+  extend CalculatorOptions {
+    optional GlAnimationOverlayCalculatorOptions ext = 174760573;
+  }
+
+  // Default aspect ratio of rendering target width over height.
+  // This specific value is for 3:4 view. Do not change this default value.
+  optional float aspect_ratio = 1 [default = 0.75];
+  // Default vertical field of view in degrees. This specific default value
+  // is arbitrary. Do not change this default value. If you want to use
+  // a different vertical_fov_degrees, set it in the options.
+  optional float vertical_fov_degrees = 2 [default = 70.0];
+
+  // Perspective projection matrix z-clipping near plane value.
+  optional float z_clipping_plane_near = 3 [default = 0.1];
+  // Perspective projection matrix z-clipping far plane value.
+  optional float z_clipping_plane_far = 4 [default = 1000.0];
+
+  // Speed at which to play the animation (in frames per second).
+  optional float animation_speed_fps = 5 [default = 25.0];
+}
diff --git a/mediapipe/graphs/object_detection_3d/calculators/model_matrix.proto b/mediapipe/graphs/object_detection_3d/calculators/model_matrix.proto
new file mode 100644
index 0000000..406cc9f
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/calculators/model_matrix.proto
@@ -0,0 +1,48 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+message TimedModelMatrixProto {
+  // 4x4 model matrix stored in ROW major order.
+  repeated float matrix_entries = 1 [packed = true];
+  // Timestamp of this model matrix in milliseconds.
+  optional int64 time_msec = 2 [default = 0];
+  // Unique per object id
+  optional int32 id = 3 [default = -1];
+}
+
+message TimedModelMatrixProtoList {
+  repeated TimedModelMatrixProto model_matrix = 1;
+}
+
+// For convenience, when the desired information or transformation can be
+// encoded into vectors (e.g. when the matrix represents a scale or Euler-angle-
+// based rotation operation.)
+message TimedVectorProto {
+  // The vector values themselves.
+  repeated float vector_entries = 1 [packed = true];
+
+  // Timestamp of this vector in milliseconds.
+  optional int64 time_msec = 2 [default = 0];
+
+  // Unique per object id
+  optional int32 id = 3 [default = -1];
+}
+
+message TimedVectorProtoList {
+  repeated TimedVectorProto vector_list = 1;
+}
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/BUILD b/mediapipe/graphs/object_detection_3d/obj_parser/BUILD
new file mode 100644
index 0000000..3b84cc8
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/obj_parser/BUILD
@@ -0,0 +1,33 @@
+# Copyright 2021 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+java_library(
+    name = "obj_parser_lib",
+    srcs = [
+        "ObjParserMain.java",
+        "SimpleObjParser.java",
+    ],
+    javacopts = ["-Xep:DefaultPackage:OFF"],
+)
+
+java_binary(
+    name = "ObjParser",
+    javacopts = ["-Xep:DefaultPackage:OFF"],
+    main_class = "ObjParserMain",
+    runtime_deps = [
+        ":obj_parser_lib",
+    ],
+)
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/ObjParserMain.java b/mediapipe/graphs/object_detection_3d/obj_parser/ObjParserMain.java
new file mode 100644
index 0000000..80e639d
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/obj_parser/ObjParserMain.java
@@ -0,0 +1,205 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import static java.nio.charset.StandardCharsets.UTF_8;
+
+import java.io.BufferedWriter;
+import java.io.File;
+import java.io.FileFilter;
+import java.io.FileOutputStream;
+import java.io.OutputStream;
+import java.io.OutputStreamWriter;
+import java.io.PrintWriter;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.util.ArrayList;
+import java.util.Arrays;
+
+/**
+ * Class for running desktop-side parsing/packing routines on .obj AR assets. Usage: ObjParser
+ * --input_dir=[INPUT_DIRECTORY] --output_dir=[OUTPUT_DIRECTORY] where INPUT_DIRECTORY is the folder
+ * with asset obj files to process, and OUTPUT_DIRECTORY is the folder where processed asset uuu
+ * file should be placed.
+ *
+ * <p>NOTE: Directories are assumed to be absolute paths.
+ */
+public final class ObjParserMain {
+  // Simple FileFilter implementation to let us walk over only our .obj files in a particular
+  // directory.
+  private static final class ObjFileFilter implements FileFilter {
+    ObjFileFilter() {
+      // Nothing to do here.
+    }
+
+    @Override
+    public boolean accept(File file) {
+      return file.getName().endsWith(".obj");
+    }
+  }
+
+  // File extension for binary output files; tagged onto end of initial file extension.
+  private static final String BINARY_FILE_EXT = ".uuu";
+  private static final String INPUT_DIR_FLAG = "--input_dir=";
+  private static final String OUTPUT_DIR_FLAG = "--output_dir=";
+  private static final float DEFAULT_VERTEX_SCALE_FACTOR = 30.0f;
+  private static final double NS_TO_SECONDS = 1e9;
+
+  public final PrintWriter writer;
+
+  public ObjParserMain() {
+    super();
+    this.writer = new PrintWriter(new BufferedWriter(new OutputStreamWriter(System.out, UTF_8)));
+  }
+
+  // Simple overridable logging function.
+  protected void logString(String infoLog) {
+    writer.println(infoLog);
+  }
+
+  /*
+   * Main program logic: parse command-line arguments and perform actions.
+   */
+  public void run(String inDirectory, String outDirectory) {
+    if (inDirectory.isEmpty()) {
+      logString("Error: Must provide input directory with " + INPUT_DIR_FLAG);
+      return;
+    }
+    if (outDirectory.isEmpty()) {
+      logString("Error: Must provide output directory with " + OUTPUT_DIR_FLAG);
+      return;
+    }
+
+    File dirAsFile = new File(inDirectory);
+    ObjFileFilter objFileFilter = new ObjFileFilter();
+    File[] objFiles = dirAsFile.listFiles(objFileFilter);
+
+    FileOutputStream outputStream = null;
+    logString("Parsing directory: " + inDirectory);
+    // We need frames processed in correct order.
+    Arrays.sort(objFiles);
+
+    for (File objFile : objFiles) {
+      String fileName = objFile.getAbsolutePath();
+
+      // Just take the file name of the first processed frame.
+      if (outputStream == null) {
+        String outputFileName = outDirectory + objFile.getName() + BINARY_FILE_EXT;
+        try {
+          // Create new file here, if we can.
+          outputStream = new FileOutputStream(outputFileName);
+          logString("Created outfile: " + outputFileName);
+        } catch (Exception e) {
+          logString("Error creating outfile: " + e.toString());
+          e.printStackTrace(writer);
+          return;
+        }
+      }
+
+      // Process each file into the stream.
+      logString("Processing file: " + fileName);
+      processFile(fileName, outputStream);
+    }
+
+    // Finally close the stream out.
+    try {
+      if (outputStream != null) {
+        outputStream.close();
+      }
+    } catch (Exception e) {
+      logString("Error trying to close output stream: " + e.toString());
+      e.printStackTrace(writer);
+    }
+  }
+
+  /*
+   * Entrypoint for command-line executable.
+   */
+  public static void main(String[] args) {
+    // Parse flags
+    String inDirectory = "";
+    String outDirectory = "";
+    for (int i = 0; i < args.length; i++) {
+      if (args[i].startsWith(INPUT_DIR_FLAG)) {
+        inDirectory = args[i].substring(INPUT_DIR_FLAG.length());
+        // Make sure this will be treated as a directory
+        if (!inDirectory.endsWith("/")) {
+          inDirectory += "/";
+        }
+      }
+      if (args[i].startsWith(OUTPUT_DIR_FLAG)) {
+        outDirectory = args[i].substring(OUTPUT_DIR_FLAG.length());
+        // Make sure this will be treated as a directory
+        if (!outDirectory.endsWith("/")) {
+          outDirectory += "/";
+        }
+      }
+    }
+    ObjParserMain parser = new ObjParserMain();
+    parser.run(inDirectory, outDirectory);
+    parser.writer.flush();
+  }
+
+  /*
+   * Internal helper function to parse a .obj from an infile name and stream the resulting data
+   * directly out in binary-dump format to outputStream.
+   */
+  private void processFile(String infileName, OutputStream outputStream) {
+    long start = System.nanoTime();
+
+    // First we parse the obj.
+    SimpleObjParser objParser = new SimpleObjParser(infileName, DEFAULT_VERTEX_SCALE_FACTOR);
+    if (!objParser.parse()) {
+      logString("Error parsing .obj file before processing");
+      return;
+    }
+
+    final float[] vertices = objParser.getVertices();
+    final float[] textureCoords = objParser.getTextureCoords();
+    final ArrayList<Short> triangleList = objParser.getTriangles();
+
+    // Overall byte count to stream: 12 for the 3 list-length ints, and then 4 for each vertex and
+    // texCoord int, and finally 2 for each triangle index short.
+    final int bbSize =
+        12 + 4 * vertices.length + 4 * textureCoords.length + 2 * triangleList.size();
+
+    // Ensure ByteBuffer is native order, just like we want to read it in, but is NOT direct, so
+    // we can call .array() on it.
+    ByteBuffer bb = ByteBuffer.allocate(bbSize);
+    bb.order(ByteOrder.nativeOrder());
+
+    bb.putInt(vertices.length);
+    bb.putInt(textureCoords.length);
+    bb.putInt(triangleList.size());
+    logString(String.format("Writing... Vertices: %d, TextureCoords: %d, Indices: %d.%n",
+        vertices.length, textureCoords.length, triangleList.size()));
+    for (float vertex : vertices) {
+      bb.putFloat(vertex);
+    }
+    for (float textureCoord : textureCoords) {
+      bb.putFloat(textureCoord);
+    }
+    for (Short vertexIndex : triangleList) {
+      bb.putShort(vertexIndex.shortValue());
+    }
+    bb.position(0);
+    try {
+      outputStream.write(bb.array(), 0, bbSize);
+      logString(String.format("Processing successful!  Took %.4f seconds.%n",
+          (System.nanoTime() - start) / NS_TO_SECONDS));
+    } catch (Exception e) {
+      logString("Error writing during processing: " + e.toString());
+      e.printStackTrace(writer);
+    }
+  }
+}
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/SimpleObjParser.java b/mediapipe/graphs/object_detection_3d/obj_parser/SimpleObjParser.java
new file mode 100644
index 0000000..937fdff
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/obj_parser/SimpleObjParser.java
@@ -0,0 +1,386 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import static java.nio.charset.StandardCharsets.UTF_8;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * Class for parsing a single .obj file into openGL-usable pieces.
+ *
+ * <p>Usage:
+ *
+ * <p>SimpleObjParser objParser = new SimpleObjParser("animations/cow/cow320.obj", .015f);
+ *
+ * <p>if (objParser.parse()) { ... }
+ */
+public class SimpleObjParser {
+  private static class ShortPair {
+    private final Short first;
+    private final Short second;
+
+    public ShortPair(Short newFirst, Short newSecond) {
+      first = newFirst;
+      second = newSecond;
+    }
+
+    public Short getFirst() {
+      return first;
+    }
+
+    public Short getSecond() {
+      return second;
+    }
+  }
+
+  private static final String TAG = SimpleObjParser.class.getSimpleName();
+  private static final boolean DEBUG = false;
+  private static final int INVALID_INDEX = -1;
+  private static final int POSITIONS_COORDS_PER_VERTEX = 3;
+  private static final int TEXTURE_COORDS_PER_VERTEX = 2;
+  private final String fileName;
+
+  // Since .obj doesn't tie together texture coordinates and vertex
+  // coordinates, but OpenGL does, we need to keep a map of all such pairings that occur in
+  // our face list.
+  private final HashMap<ShortPair, Short> vertexTexCoordMap;
+
+  // Internal (de-coupled) unique vertices and texture coordinates
+  private ArrayList<Float> vertices;
+  private ArrayList<Float> textureCoords;
+
+  // Data we expose to openGL for rendering
+  private float[] finalizedVertices;
+  private float[] finalizedTextureCoords;
+  private ArrayList<Short> finalizedTriangles;
+
+  // So we only display warnings about dropped w-coordinates once
+  private boolean vertexCoordIgnoredWarning;
+  private boolean textureCoordIgnoredWarning;
+  private boolean startedProcessingFaces;
+
+  private int numPrimitiveVertices;
+  private int numPrimitiveTextureCoords;
+  private int numPrimitiveFaces;
+
+  // For scratchwork, so we don't have to keep reallocating
+  private float[] tempCoords;
+
+  // We scale all our position coordinates uniformly by this factor
+  private float objectUniformScaleFactor;
+
+  public SimpleObjParser(String objFile, float scaleFactor) {
+    objectUniformScaleFactor = scaleFactor;
+
+    fileName = objFile;
+    vertices = new ArrayList<Float>();
+    textureCoords = new ArrayList<Float>();
+
+    vertexTexCoordMap = new HashMap<ShortPair, Short>();
+    finalizedTriangles = new ArrayList<Short>();
+
+    tempCoords = new float[Math.max(POSITIONS_COORDS_PER_VERTEX, TEXTURE_COORDS_PER_VERTEX)];
+    numPrimitiveFaces = 0;
+
+    vertexCoordIgnoredWarning = false;
+    textureCoordIgnoredWarning = false;
+    startedProcessingFaces = false;
+  }
+
+  // Simple helper wrapper function
+  private void debugLogString(String message) {
+    if (DEBUG) {
+      System.out.println(message);
+    }
+  }
+
+  private void parseVertex(String[] linePieces) {
+    // Note: Traditionally xyzw is acceptable as a format, with w defaulting to 1.0, but for now
+    // we only parse xyz.
+    if (linePieces.length < POSITIONS_COORDS_PER_VERTEX + 1
+        || linePieces.length > POSITIONS_COORDS_PER_VERTEX + 2) {
+      System.out.println("Malformed vertex coordinate specification, assuming xyz format only.");
+      return;
+    } else if (linePieces.length == POSITIONS_COORDS_PER_VERTEX + 2 && !vertexCoordIgnoredWarning) {
+      System.out.println(
+          "Only x, y, and z parsed for vertex coordinates; w coordinates will be ignored.");
+      vertexCoordIgnoredWarning = true;
+    }
+
+    boolean success = true;
+    try {
+      for (int i = 1; i < POSITIONS_COORDS_PER_VERTEX + 1; i++) {
+        tempCoords[i - 1] = Float.parseFloat(linePieces[i]);
+      }
+    } catch (NumberFormatException e) {
+      success = false;
+      System.out.println("Malformed vertex coordinate error: " + e.toString());
+    }
+
+    if (success) {
+      for (int i = 0; i < POSITIONS_COORDS_PER_VERTEX; i++) {
+        vertices.add(Float.valueOf(tempCoords[i] * objectUniformScaleFactor));
+      }
+    }
+  }
+
+  private void parseTextureCoordinate(String[] linePieces) {
+    // Similar to vertices, uvw is acceptable as a format, with w defaulting to 0.0, but for now we
+    // only parse uv.
+    if (linePieces.length < TEXTURE_COORDS_PER_VERTEX + 1
+        || linePieces.length > TEXTURE_COORDS_PER_VERTEX + 2) {
+      System.out.println("Malformed texture coordinate specification, assuming uv format only.");
+      return;
+    } else if (linePieces.length == (TEXTURE_COORDS_PER_VERTEX + 2)
+        && !textureCoordIgnoredWarning) {
+      debugLogString("Only u and v parsed for texture coordinates; w coordinates will be ignored.");
+      textureCoordIgnoredWarning = true;
+    }
+
+    boolean success = true;
+    try {
+      for (int i = 1; i < TEXTURE_COORDS_PER_VERTEX + 1; i++) {
+        tempCoords[i - 1] = Float.parseFloat(linePieces[i]);
+      }
+    } catch (NumberFormatException e) {
+      success = false;
+      System.out.println("Malformed texture coordinate error: " + e.toString());
+    }
+
+    if (success) {
+      // .obj files treat (0,0) as top-left, compared to bottom-left for openGL.  So invert "v"
+      // texture coordinate only here.
+      textureCoords.add(Float.valueOf(tempCoords[0]));
+      textureCoords.add(Float.valueOf(1.0f - tempCoords[1]));
+    }
+  }
+
+  // Will return INVALID_INDEX if error occurs, and otherwise will return finalized (combined)
+  // index, adding and hashing new combinations as it sees them.
+  private short parseAndProcessCombinedVertexCoord(String coordString) {
+    String[] coords = coordString.split("/");
+    try {
+      // Parse vertex and texture indices; 1-indexed from front if positive and from end of list if
+      // negative.
+      short vertexIndex = Short.parseShort(coords[0]);
+      short textureIndex = Short.parseShort(coords[1]);
+      if (vertexIndex > 0) {
+        vertexIndex--;
+      } else {
+        vertexIndex = (short) (vertexIndex + numPrimitiveVertices);
+      }
+      if (textureIndex > 0) {
+        textureIndex--;
+      } else {
+        textureIndex = (short) (textureIndex + numPrimitiveTextureCoords);
+      }
+
+      // Combine indices and look up in pair map.
+      ShortPair indexPair = new ShortPair(Short.valueOf(vertexIndex), Short.valueOf(textureIndex));
+      Short combinedIndex = vertexTexCoordMap.get(indexPair);
+      if (combinedIndex == null) {
+        short numIndexPairs = (short) vertexTexCoordMap.size();
+        vertexTexCoordMap.put(indexPair, numIndexPairs);
+        return numIndexPairs;
+      } else {
+        return combinedIndex.shortValue();
+      }
+    } catch (NumberFormatException e) {
+      // Failure to parse coordinates as shorts
+      return INVALID_INDEX;
+    }
+  }
+
+  // Note: it is assumed that face list occurs AFTER vertex and texture coordinate lists finish in
+  //  the obj file format.
+  private void parseFace(String[] linePieces) {
+    if (linePieces.length < 4) {
+      System.out.println("Malformed face index list: there must be at least 3 indices per face");
+      return;
+    }
+
+    short[] faceIndices = new short[linePieces.length - 1];
+    boolean success = true;
+    for (int i = 1; i < linePieces.length; i++) {
+      short faceIndex = parseAndProcessCombinedVertexCoord(linePieces[i]);
+
+      if (faceIndex < 0) {
+        System.out.println(faceIndex);
+        System.out.println("Malformed face index: " + linePieces[i]);
+        success = false;
+        break;
+      }
+      faceIndices[i - 1] = faceIndex;
+    }
+
+    if (success) {
+      numPrimitiveFaces++;
+      // Manually triangulate the face under the assumption that the points are coplanar, the poly
+      // is convex, and the points are listed in either clockwise or anti-clockwise orientation.
+      for (int i = 1; i < faceIndices.length - 1; i++) {
+        // We use a triangle fan here, so first point is part of all triangles
+        finalizedTriangles.add(faceIndices[0]);
+        finalizedTriangles.add(faceIndices[i]);
+        finalizedTriangles.add(faceIndices[i + 1]);
+      }
+    }
+  }
+
+  // Iterate over map and reconstruct proper vertex/texture coordinate pairings.
+  private boolean constructFinalCoordinatesFromMap() {
+    final int numIndexPairs = vertexTexCoordMap.size();
+    // XYZ vertices and UV texture coordinates
+    finalizedVertices = new float[POSITIONS_COORDS_PER_VERTEX * numIndexPairs];
+    finalizedTextureCoords = new float[TEXTURE_COORDS_PER_VERTEX * numIndexPairs];
+    try {
+      for (Map.Entry<ShortPair, Short> entry : vertexTexCoordMap.entrySet()) {
+        ShortPair indexPair = entry.getKey();
+        short rawVertexIndex = indexPair.getFirst().shortValue();
+        short rawTexCoordIndex = indexPair.getSecond().shortValue();
+        short finalIndex = entry.getValue().shortValue();
+        for (int i = 0; i < POSITIONS_COORDS_PER_VERTEX; i++) {
+          finalizedVertices[POSITIONS_COORDS_PER_VERTEX * finalIndex + i]
+              = vertices.get(rawVertexIndex * POSITIONS_COORDS_PER_VERTEX + i);
+        }
+        for (int i = 0; i < TEXTURE_COORDS_PER_VERTEX; i++) {
+          finalizedTextureCoords[TEXTURE_COORDS_PER_VERTEX * finalIndex + i]
+              = textureCoords.get(rawTexCoordIndex * TEXTURE_COORDS_PER_VERTEX + i);
+        }
+      }
+    } catch (NumberFormatException e) {
+      System.out.println("Malformed index in vertex/texture coordinate mapping.");
+      return false;
+    }
+    return true;
+  }
+
+  /**
+   * Returns the vertex position coordinate list (x1, y1, z1, x2, y2, z2, ...) after a successful
+   * call to parse().
+   */
+  public float[] getVertices() {
+    return finalizedVertices;
+  }
+
+  /**
+   * Returns the vertex texture coordinate list (u1, v1, u2, v2, ...) after a successful call to
+   * parse().
+   */
+  public float[] getTextureCoords() {
+    return finalizedTextureCoords;
+  }
+
+  /**
+   * Returns the list of indices (a1, b1, c1, a2, b2, c2, ...) after a successful call to parse().
+   * Each (a, b, c) triplet specifies a triangle to be rendered, with a, b, and c Short objects used
+   * to index into the coordinates returned by getVertices() and getTextureCoords().<p></p>
+   * For example, a Short index representing 5 should be used to index into vertices[15],
+   * vertices[16], and vertices[17], as well as textureCoords[10] and textureCoords[11].
+   */
+  public ArrayList<Short> getTriangles() {
+    return finalizedTriangles;
+  }
+
+  /**
+   * Attempts to locate and read the specified .obj file, and parse it accordingly.  None of the
+   * getter functions in this class will return valid results until a value of true is returned
+   * from this function.
+   * @return true on success.
+   */
+  public boolean parse() {
+    boolean success = true;
+    BufferedReader reader = null;
+    try {
+      reader = Files.newBufferedReader(Paths.get(fileName), UTF_8);
+      String line;
+      while ((line = reader.readLine()) != null) {
+        // Skip over lines with no characters
+        if (line.length() < 1) {
+          continue;
+        }
+
+        // Ignore comment lines entirely
+        if (line.charAt(0) == '#') {
+          continue;
+        }
+
+        // Split into pieces based on whitespace, and process according to first command piece
+        String[] linePieces = line.split(" +");
+        switch (linePieces[0]) {
+          case "v":
+            // Add vertex
+            if (startedProcessingFaces) {
+              throw new IOException("Vertices must all be declared before faces in obj files.");
+            }
+            parseVertex(linePieces);
+            break;
+          case "vt":
+            // Add texture coordinate
+            if (startedProcessingFaces) {
+              throw new IOException(
+                  "Texture coordinates must all be declared before faces in obj files.");
+            }
+            parseTextureCoordinate(linePieces);
+            break;
+          case "f":
+            // Vertex and texture coordinate lists should be locked into place by now
+            if (!startedProcessingFaces) {
+              startedProcessingFaces = true;
+              numPrimitiveVertices = vertices.size() / POSITIONS_COORDS_PER_VERTEX;
+              numPrimitiveTextureCoords = textureCoords.size() / TEXTURE_COORDS_PER_VERTEX;
+            }
+            // Add face
+            parseFace(linePieces);
+            break;
+          default:
+            // Unknown or unused directive: ignoring
+            // Note: We do not yet process vertex normals or curves, so we ignore {vp, vn, s}
+            // Note: We assume only a single object, so we ignore {g, o}
+            // Note: We also assume a single texture, which we process independently, so we ignore
+            // {mtllib, usemtl}
+            break;
+        }
+      }
+
+      // If we made it all the way through, then we have a vertex-to-tex-coord pair mapping, so
+      // construct our final vertex and texture coordinate lists now.
+      success = constructFinalCoordinatesFromMap();
+
+    } catch (IOException e) {
+      success = false;
+      System.out.println("Failure to parse obj file: " + e.toString());
+    } finally {
+      try {
+        if (reader != null) {
+          reader.close();
+        }
+      } catch (IOException e) {
+        System.out.println("Couldn't close reader");
+      }
+    }
+    if (success) {
+      debugLogString("Successfully parsed " + numPrimitiveVertices + " vertices and "
+          + numPrimitiveTextureCoords + " texture coordinates into " + vertexTexCoordMap.size()
+          + " combined vertices and " + numPrimitiveFaces + " faces, represented as a mesh of "
+          + finalizedTriangles.size() / 3 + " triangles.");
+    }
+    return success;
+  }
+}
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh b/mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh
new file mode 100755
index 0000000..1573387
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+
+# Copyright 2021 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# The SimpleObjParser expects the obj commands to follow v/vt/f order. This
+# little script will read all the obj files in a directory and sort the
+# existing obj commands inside them to also follow this order (so all v lines
+# will appear before all vt lines, which will appear before all f lines).
+
+# Usage: ./obj_cleanup.sh input_folder output_folder
+# input_folder and output_folder paths can be absolute or relative.
+
+input_folder=$1
+output_folder=$2
+if [[ "${input_folder}" == "" ]]; then
+  echo "input_folder must be defined.  Usage: ./obj_cleanup.sh input_folder output_folder"
+  exit 1
+fi
+if [[ "${output_folder}" == "" ]]; then
+  echo "output_folder must be defined.  Usage: ./obj_cleanup.sh input_folder output_folder"
+  exit 1
+fi
+
+# Find all the obj files and remove the directory name
+# Interestingly, piping | sed 's!.obj!!  also removed the extension obj too.
+find "${input_folder}" -name "*.obj" | sed 's!.*/!!' | sort |
+while IFS= read -r filename; do
+  echo "Clean up ${filename}"
+  cat "${input_folder}/${filename}" | grep 'v ' > "${output_folder}/${filename}"
+  cat "${input_folder}/${filename}" | grep 'vt ' >> "${output_folder}/${filename}"
+  cat "${input_folder}/${filename}" | grep 'f ' >> "${output_folder}/${filename}"
+done
diff --git a/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt b/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt
new file mode 100644
index 0000000..10b11de
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt
@@ -0,0 +1,122 @@
+# MediaPipe graph that performs box tracking with TensorFlow Lite on GPU.
+
+# Images coming into and out of the graph.
+input_stream: "input_video"
+input_stream: "WIDTH:input_width"
+input_stream: "HEIGHT:input_height"
+input_side_packet: "LABELS_CSV:allowed_labels"
+input_side_packet: "MODEL_SCALE:model_scale"
+input_side_packet: "MODEL_TRANSFORMATION:model_transformation"
+input_side_packet: "TEXTURE:box_texture"
+input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
+input_side_packet: "ANIMATION_ASSET:box_asset_name"
+input_side_packet: "MASK_TEXTURE:obj_texture"
+input_side_packet: "MASK_ASSET:obj_asset_name"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Crops the image from the center to the size WIDTHxHEIGHT.
+node: {
+  calculator: "ImageCroppingCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  output_stream: "IMAGE_GPU:throttled_input_video_3x4"
+  input_stream: "WIDTH:input_width"
+  input_stream: "HEIGHT:input_height"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageCroppingCalculatorOptions] {
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+node {
+  calculator: "ObjectronGpuSubgraph"
+  input_stream: "IMAGE_GPU:throttled_input_video_3x4"
+  input_side_packet: "LABELS_CSV:allowed_labels"
+  input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
+  output_stream: "FRAME_ANNOTATION:lifted_objects"
+}
+
+# The rendering nodes:
+# We are rendering two meshes: 1) a 3D bounding box, which we overlay directly
+# on the texture, and 2) a virtual object, which we use as an occlusion mask.
+# These models are designed using different tools, so we supply a transformation
+# to bring both of them to the Objectron's coordinate system.
+
+# Creates a model matrices for the tracked object given the lifted 3D points.
+# This calculator does two things: 1) Estimates object's pose (orientation,
+# translation, and scale) from the 3D vertices, and
+# 2) bring the object from the objectron's coordinate system to the renderer
+# (OpenGL) coordinate system. Since the final goal is to render a mesh file on
+# top of the object, we also supply a transformation to bring the mesh to the
+# objectron's coordinate system, and rescale mesh to the unit size.
+node {
+  calculator: "AnnotationsToModelMatricesCalculator"
+  input_stream: "ANNOTATIONS:lifted_objects"
+  output_stream: "MODEL_MATRICES:model_matrices"
+  node_options: {
+    [type.googleapis.com/mediapipe.AnnotationsToModelMatricesCalculatorOptions] {
+      # Re-scale the CAD model to the size of a unit box
+      model_scale: [0.04, 0.04, 0.04]
+      # Bring the box CAD model to objectron's coordinate system. This
+      # is equivalent of -pi/2 rotation along the y-axis (right-hand rule):
+      # Eigen::AngleAxisf(-M_PI / 2., Eigen::Vector3f::UnitY())
+      model_transformation: [0.0,  0.0, -1.0,  0.0]
+      model_transformation: [0.0,  1.0,  0.0,  0.0]
+      model_transformation: [1.0,  0.0,  0.0,  0.0]
+      model_transformation: [0.0,  0.0,  0.0,  1.0]
+    }
+  }
+}
+
+# Compute the model matrices for the CAD model of the virtual object, to be used
+# as an occlusion mask. The model will be rendered at the exact same location as
+# the bounding box.
+node {
+  calculator: "AnnotationsToModelMatricesCalculator"
+  input_stream: "ANNOTATIONS:lifted_objects"
+  input_side_packet: "MODEL_SCALE:model_scale"
+  input_side_packet: "MODEL_TRANSFORMATION:model_transformation"
+  output_stream: "MODEL_MATRICES:mask_model_matrices"
+}
+
+# Render everything together. First we render the 3D bounding box animation,
+# then we render the occlusion mask.
+node: {
+  calculator: "GlAnimationOverlayCalculator"
+  input_stream: "VIDEO:throttled_input_video_3x4"
+  input_stream: "MODEL_MATRICES:model_matrices"
+  input_stream: "MASK_MODEL_MATRICES:mask_model_matrices"
+  output_stream: "output_video"
+  input_side_packet: "TEXTURE:box_texture"
+  input_side_packet: "ANIMATION_ASSET:box_asset_name"
+  input_side_packet: "MASK_TEXTURE:obj_texture"
+  input_side_packet: "MASK_ASSET:obj_asset_name"
+  node_options: {
+    [type.googleapis.com/mediapipe.GlAnimationOverlayCalculatorOptions] {
+      aspect_ratio: 0.75
+      vertical_fov_degrees: 70.
+      animation_speed_fps: 25
+    }
+  }
+}
diff --git a/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt b/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt
new file mode 100644
index 0000000..bda02b2
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt
@@ -0,0 +1,133 @@
+# MediaPipe object detection 3D with tracking graph.
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+input_stream: "input_width"
+input_stream: "input_height"
+output_stream: "output_video"
+
+# Crops the image from the center to the size WIDTHxHEIGHT.
+node: {
+  calculator: "ImageCroppingCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "IMAGE_GPU:input_video_4x3"
+  input_stream: "WIDTH:input_width"
+  input_stream: "HEIGHT:input_height"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageCroppingCalculatorOptions] {
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+# Creates a copy of the input_video stream. At the end of the graph, the
+# GlAnimationOverlayCalculator will consume the input_video texture and draws
+# on top of it.
+node: {
+  calculator: "GlScalerCalculator"
+  input_stream: "VIDEO:input_video_4x3"
+  output_stream: "VIDEO:input_video_copy"
+}
+
+# Resamples the images by specific frame rate. This calculator is used to
+# control the frequecy of subsequent calculators/subgraphs, e.g. less power
+# consumption for expensive process.
+node {
+  calculator: "PacketResamplerCalculator"
+  input_stream: "DATA:input_video_copy"
+  output_stream: "DATA:sampled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.PacketResamplerCalculatorOptions] {
+      frame_rate: 5
+    }
+  }
+}
+
+node {
+  calculator: "ObjectronDetection1StageSubgraphGpu"
+  input_stream: "IMAGE_GPU:sampled_input_video"
+  output_stream: "ANNOTATIONS:objects"
+}
+
+node {
+  calculator: "ObjectronTracking1StageSubgraphGpu"
+  input_stream: "FRAME_ANNOTATION:objects"
+  input_stream: "IMAGE_GPU:input_video_copy"
+  output_stream: "LIFTED_FRAME_ANNOTATION:lifted_tracked_objects"
+}
+
+# The rendering nodes:
+# We are rendering two meshes: 1) a 3D bounding box, which we overlay directly
+# on the texture, and 2) a shoe CAD model, which we use as an occlusion mask.
+# These models are designed using different tools, so we supply a transformation
+# to bring both of them to the Objectron's coordinate system.
+
+# Creates a model matrices for the tracked object given the lifted 3D points.
+# This calculator does two things: 1) Estimates object's pose (orientation,
+# translation, and scale) from the 3D vertices, and
+# 2) bring the object from the objectron's coordinate system to the renderer
+# (OpenGL) coordinate system. Since the final goal is to render a mesh file on
+# top of the object, we also supply a transformation to bring the mesh to the
+# objectron's coordinate system, and rescale mesh to the unit size.
+node {
+  calculator: "AnnotationsToModelMatricesCalculator"
+  input_stream: "ANNOTATIONS:lifted_tracked_objects"
+  output_stream: "MODEL_MATRICES:model_matrices"
+  node_options: {
+    [type.googleapis.com/mediapipe.AnnotationsToModelMatricesCalculatorOptions] {
+      # Re-scale the CAD model to the size of a unit box
+      model_scale: [0.05, 0.05, 0.05]
+      # Bring the box CAD model to objectron's coordinate system. This
+      # is equivalent of -pi/2 rotation along the y-axis (right-hand rule):
+      # Eigen::AngleAxisf(-M_PI / 2., Eigen::Vector3f::UnitY())
+      model_transformation: [0.0,  0.0, -1.0,  0.0]
+      model_transformation: [0.0,  1.0,  0.0,  0.0]
+      model_transformation: [1.0,  0.0,  0.0,  0.0]
+      model_transformation: [0.0,  0.0,  0.0,  1.0]
+    }
+  }
+}
+
+# Compute the model matrices for the CAD model of the chair, to be used as an
+# occlusion mask. The model will be rendered at the exact same location as the
+# bounding box.
+node {
+  calculator: "AnnotationsToModelMatricesCalculator"
+  input_stream: "ANNOTATIONS:lifted_tracked_objects"
+  output_stream: "MODEL_MATRICES:mask_model_matrices"
+  node_options: {
+    [type.googleapis.com/mediapipe.AnnotationsToModelMatricesCalculatorOptions] {
+      # Re-scale the CAD model to the size of a unit box
+      model_scale: [0.15, 0.1, 0.15]
+      # Bring the CAD model to Deep Pursuit 3D's coordinate system. This
+      # is equivalent of -pi/2 rotation along the x-axis:
+      # Eigen::AngleAxisf(-M_PI / 2., Eigen::Vector3f::UnitX())
+      model_transformation: [1.0,  0.0,  0.0,  0.0]
+      model_transformation: [0.0,  1.0,  0.0,  -10.0]
+      model_transformation: [0.0,  0.0,  -1.0,  0.0]
+      model_transformation: [0.0,  0.0,  0.0,  1.0]
+    }
+  }
+}
+
+# Render everything together. First we render the 3D bounding box animation,
+# then we render the occlusion mask.
+node:{
+  calculator:"GlAnimationOverlayCalculator"
+  input_stream:"VIDEO:input_video_4x3"
+  input_stream:"MODEL_MATRICES:model_matrices"
+  input_stream:"MASK_MODEL_MATRICES:mask_model_matrices"
+  output_stream:"output_video"
+  input_side_packet:"TEXTURE:box_texture"
+  input_side_packet:"ANIMATION_ASSET:box_asset_name"
+  input_side_packet:"MASK_TEXTURE:obj_texture"
+  input_side_packet:"MASK_ASSET:obj_asset_name"
+  node_options: {
+    [type.googleapis.com/mediapipe.GlAnimationOverlayCalculatorOptions] {
+    # Output resolution is 480x640 with the aspect ratio of 0.75
+      aspect_ratio: 0.75
+      vertical_fov_degrees: 70.
+      animation_speed_fps: 25
+    }
+  }
+}
diff --git a/mediapipe/graphs/object_detection_3d/objectron_desktop_cpu.pbtxt b/mediapipe/graphs/object_detection_3d/objectron_desktop_cpu.pbtxt
new file mode 100644
index 0000000..0a962d7
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/objectron_desktop_cpu.pbtxt
@@ -0,0 +1,60 @@
+# MediaPipe Objectron 3D object detection on Desktop CPU.
+input_side_packet: "INPUT_FILE_PATH:input_video_path"
+input_side_packet: "FILE_PATH:0:box_landmark_model_path"
+input_side_packet: "LABELS_CSV:allowed_labels"
+input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+
+# Generates side packet with max number of objects to detect/track.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:max_num_objects"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 5 }
+    }
+  }
+}
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+# Run Objectron subgraph.
+node {
+  calculator: "ObjectronCpuSubgraph"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "MODEL_PATH:box_landmark_model_path"
+  input_side_packet: "LABELS_CSV:allowed_labels"
+  input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
+  output_stream: "MULTI_LANDMARKS:box_landmarks"
+  output_stream: "NORM_RECTS:box_rect"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see renderer_cpu.pbtxt).
+node {
+  calculator: "RendererSubgraph"
+  input_stream: "IMAGE:input_video"
+  input_stream: "MULTI_LANDMARKS:box_landmarks"
+  input_stream: "NORM_RECTS:box_rect"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/object_detection_3d/subgraphs/BUILD b/mediapipe/graphs/object_detection_3d/subgraphs/BUILD
new file mode 100644
index 0000000..524ef9f
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/subgraphs/BUILD
@@ -0,0 +1,37 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "renderer_cpu",
+    graph = "renderer_cpu.pbtxt",
+    register_as = "RendererSubgraph",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+    ],
+)
diff --git a/mediapipe/graphs/object_detection_3d/subgraphs/renderer_cpu.pbtxt b/mediapipe/graphs/object_detection_3d/subgraphs/renderer_cpu.pbtxt
new file mode 100644
index 0000000..0f275a7
--- /dev/null
+++ b/mediapipe/graphs/object_detection_3d/subgraphs/renderer_cpu.pbtxt
@@ -0,0 +1,75 @@
+# MediaPipe Objectron vertices/landmarks rendering CPU subgraph.
+
+type: "RendererSubgraph"
+
+input_stream: "IMAGE:input_image"
+input_stream: "MULTI_LANDMARKS:multi_landmarks"
+input_stream: "NORM_RECTS:multi_rect"
+output_stream: "IMAGE:output_image"
+
+# Outputs each element of multi_landmarks at a fake timestamp for the rest
+# of the graph to process. At the end of the loop, outputs the BATCH_END
+# timestamp for downstream calculators to inform them that all elements in the
+# vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITERABLE:multi_landmarks"
+  output_stream: "ITEM:single_landmarks"
+  output_stream: "BATCH_END:landmark_timestamp"
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:single_landmarks"
+  output_stream: "RENDER_DATA:single_landmark_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: [1, 2] # edge 1-2
+      landmark_connections: [1, 3] # edge 1-3
+      landmark_connections: [1, 5] # edge 1-5
+      landmark_connections: [2, 4] # edge 2-4
+      landmark_connections: [2, 6] # edge 2-6
+      landmark_connections: [3, 4] # edge 3-4
+      landmark_connections: [3, 7] # edge 3-7
+      landmark_connections: [4, 8] # edge 4-8
+      landmark_connections: [5, 6] # edge 5-6
+      landmark_connections: [5, 7] # edge 5-7
+      landmark_connections: [6, 8] # edge 6-8
+      landmark_connections: [7, 8] # edge 7-8
+      landmark_color { r: 255 g: 0 b: 0 }
+      connection_color { r: 0 g: 255 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+node {
+  calculator: "EndLoopRenderDataCalculator"
+  input_stream: "ITEM:single_landmark_render_data"
+  input_stream: "BATCH_END:landmark_timestamp"
+  output_stream: "ITERABLE:multi_landmarks_render_data"
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECTS:multi_rect"
+  output_stream: "RENDER_DATA:multi_rect_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_image"
+  input_stream: "VECTOR:multi_landmarks_render_data"
+  input_stream: "multi_rect_render_data"
+  output_stream: "IMAGE:output_image"
+}
diff --git a/mediapipe/graphs/pose_tracking/BUILD b/mediapipe/graphs/pose_tracking/BUILD
new file mode 100644
index 0000000..26f607c
--- /dev/null
+++ b/mediapipe/graphs/pose_tracking/BUILD
@@ -0,0 +1,56 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "pose_tracking_gpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/graphs/pose_tracking/subgraphs:pose_renderer_gpu",
+        "//mediapipe/modules/pose_landmark:pose_landmark_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "pose_tracking_gpu_binary_graph",
+    graph = "pose_tracking_gpu.pbtxt",
+    output_name = "pose_tracking_gpu.binarypb",
+    deps = [":pose_tracking_gpu_deps"],
+)
+
+cc_library(
+    name = "pose_tracking_cpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/graphs/pose_tracking/subgraphs:pose_renderer_cpu",
+        "//mediapipe/modules/pose_landmark:pose_landmark_cpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "pose_tracking_cpu_binary_graph",
+    graph = "pose_tracking_cpu.pbtxt",
+    output_name = "pose_tracking_cpu.binarypb",
+    deps = [":pose_tracking_cpu_deps"],
+)
diff --git a/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt b/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt
new file mode 100644
index 0000000..31d847e
--- /dev/null
+++ b/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt
@@ -0,0 +1,63 @@
+# MediaPipe graph that performs pose tracking with TensorFlow Lite on CPU.
+
+# CPU buffer. (ImageFrame)
+input_stream: "input_video"
+
+# Output image with rendered results. (ImageFrame)
+output_stream: "output_video"
+# Pose landmarks. (NormalizedLandmarkList)
+output_stream: "pose_landmarks"
+
+# Generates side packet to enable segmentation.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:enable_segmentation"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Subgraph that detects poses and corresponding landmarks.
+node {
+  calculator: "PoseLandmarkCpu"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "LANDMARKS:pose_landmarks"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+  output_stream: "DETECTION:pose_detection"
+  output_stream: "ROI_FROM_LANDMARKS:roi_from_landmarks"
+}
+
+# Subgraph that renders pose-landmark annotation onto the input image.
+node {
+  calculator: "PoseRendererCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LANDMARKS:pose_landmarks"
+  input_stream: "SEGMENTATION_MASK:segmentation_mask"
+  input_stream: "DETECTION:pose_detection"
+  input_stream: "ROI:roi_from_landmarks"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt b/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt
new file mode 100644
index 0000000..35be3f0
--- /dev/null
+++ b/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt
@@ -0,0 +1,63 @@
+# MediaPipe graph that performs pose tracking with TensorFlow Lite on GPU.
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# Output image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+# Pose landmarks. (NormalizedLandmarkList)
+output_stream: "pose_landmarks"
+
+# Generates side packet to enable segmentation.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:enable_segmentation"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Subgraph that detects poses and corresponding landmarks.
+node {
+  calculator: "PoseLandmarkGpu"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "LANDMARKS:pose_landmarks"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+  output_stream: "DETECTION:pose_detection"
+  output_stream: "ROI_FROM_LANDMARKS:roi_from_landmarks"
+}
+
+# Subgraph that renders pose-landmark annotation onto the input image.
+node {
+  calculator: "PoseRendererGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LANDMARKS:pose_landmarks"
+  input_stream: "SEGMENTATION_MASK:segmentation_mask"
+  input_stream: "DETECTION:pose_detection"
+  input_stream: "ROI:roi_from_landmarks"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/pose_tracking/subgraphs/BUILD b/mediapipe/graphs/pose_tracking/subgraphs/BUILD
new file mode 100644
index 0000000..fa34640
--- /dev/null
+++ b/mediapipe/graphs/pose_tracking/subgraphs/BUILD
@@ -0,0 +1,52 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "pose_renderer_gpu",
+    graph = "pose_renderer_gpu.pbtxt",
+    register_as = "PoseRendererGpu",
+    deps = [
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/image:recolor_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_scale_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_renderer_cpu",
+    graph = "pose_renderer_cpu.pbtxt",
+    register_as = "PoseRendererCpu",
+    deps = [
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/image:recolor_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_scale_calculator",
+    ],
+)
diff --git a/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_cpu.pbtxt b/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_cpu.pbtxt
new file mode 100644
index 0000000..e176765
--- /dev/null
+++ b/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_cpu.pbtxt
@@ -0,0 +1,292 @@
+# MediaPipe pose landmarks rendering subgraph.
+
+type: "PoseRendererCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:input_image"
+# Pose landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:pose_landmarks"
+# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
+input_stream: "SEGMENTATION_MASK:segmentation_mask"
+# Region of interest calculated based on landmarks. (NormalizedRect)
+input_stream: "ROI:roi"
+# Detected pose. (Detection)
+input_stream: "DETECTION:detection"
+
+# CPU image with rendered data. (ImageFrame)
+output_stream: "IMAGE:output_image"
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:input_image"
+  output_stream: "SIZE:image_size"
+}
+
+# Calculates rendering scale based on the pose roi.
+node {
+  calculator: "RectToRenderScaleCalculator"
+  input_stream: "NORM_RECT:roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "RENDER_SCALE:render_scale"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderScaleCalculatorOptions] {
+      multiplier: 0.0012
+    }
+  }
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTION:detection"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "visible_pose_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 25 }
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:pose_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 7
+      landmark_connections: 0
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 11
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 15
+      landmark_connections: 15
+      landmark_connections: 17
+      landmark_connections: 15
+      landmark_connections: 19
+      landmark_connections: 15
+      landmark_connections: 21
+      landmark_connections: 17
+      landmark_connections: 19
+      landmark_connections: 12
+      landmark_connections: 14
+      landmark_connections: 14
+      landmark_connections: 16
+      landmark_connections: 16
+      landmark_connections: 18
+      landmark_connections: 16
+      landmark_connections: 20
+      landmark_connections: 16
+      landmark_connections: 22
+      landmark_connections: 18
+      landmark_connections: 20
+      landmark_connections: 11
+      landmark_connections: 23
+      landmark_connections: 12
+      landmark_connections: 24
+      landmark_connections: 23
+      landmark_connections: 24
+      landmark_connections: 23
+      landmark_connections: 25
+      landmark_connections: 24
+      landmark_connections: 26
+      landmark_connections: 25
+      landmark_connections: 27
+      landmark_connections: 26
+      landmark_connections: 28
+      landmark_connections: 27
+      landmark_connections: 29
+      landmark_connections: 28
+      landmark_connections: 30
+      landmark_connections: 29
+      landmark_connections: 31
+      landmark_connections: 30
+      landmark_connections: 32
+      landmark_connections: 27
+      landmark_connections: 31
+      landmark_connections: 28
+      landmark_connections: 32
+
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Take left pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "landmarks_left_side"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 1 end: 4 }
+      ranges: { begin: 7 end: 8 }
+      ranges: { begin: 9 end: 10 }
+      ranges: { begin: 11 end: 12 }
+      ranges: { begin: 13 end: 14 }
+      ranges: { begin: 15 end: 16 }
+      ranges: { begin: 17 end: 18 }
+      ranges: { begin: 19 end: 20 }
+      ranges: { begin: 21 end: 22 }
+      ranges: { begin: 23 end: 24 }
+
+      combine_outputs: true
+    }
+  }
+}
+
+# Take right pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "landmarks_right_side"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 4 end: 7 }
+      ranges: { begin: 8 end: 9 }
+      ranges: { begin: 10 end: 11 }
+      ranges: { begin: 12 end: 13 }
+      ranges: { begin: 14 end: 15 }
+      ranges: { begin: 16 end: 17 }
+      ranges: { begin: 18 end: 19 }
+      ranges: { begin: 20 end: 21 }
+      ranges: { begin: 22 end: 23 }
+      ranges: { begin: 24 end: 25 }
+
+      combine_outputs: true
+    }
+  }
+}
+
+# Render pose joints as big white circles.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:visible_pose_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_background_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 5.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Render pose left side joints as orange circles (inside white ones).
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_left_side"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_left_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 138 b: 0 }
+      connection_color { r: 255 g: 138 b: 0 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Render pose right side joints as cyan circles (inside white ones).
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_right_side"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_right_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 0 g: 217 b: 231 }
+      connection_color { r: 0 g: 217 b: 231 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "RENDER_DATA:roi_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Colors the segmentation mask with the color specified in the option.
+node {
+  calculator: "RecolorCalculator"
+  input_stream: "IMAGE:input_image"
+  input_stream: "MASK:segmentation_mask"
+  output_stream: "IMAGE:segmented_image"
+  node_options: {
+    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
+      color { r: 0 g: 0 b: 255 }
+      mask_channel: RED
+      invert_mask: true
+      adjust_with_luminance: false
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:segmented_image"
+  input_stream: "detection_render_data"
+  input_stream: "landmarks_render_data"
+  input_stream: "landmarks_background_joints_render_data"
+  input_stream: "landmarks_left_joints_render_data"
+  input_stream: "landmarks_right_joints_render_data"
+  input_stream: "roi_render_data"
+  output_stream: "IMAGE:output_image"
+}
diff --git a/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt b/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt
new file mode 100644
index 0000000..4d680c6
--- /dev/null
+++ b/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt
@@ -0,0 +1,292 @@
+# MediaPipe pose landmarks rendering subgraph.
+
+type: "PoseRendererGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:input_image"
+# Pose landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:pose_landmarks"
+# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
+input_stream: "SEGMENTATION_MASK:segmentation_mask"
+# Region of interest calculated based on landmarks. (NormalizedRect)
+input_stream: "ROI:roi"
+# Detected pose. (Detection)
+input_stream: "DETECTION:detection"
+
+# GPU image with rendered data. (GpuBuffer)
+output_stream: "IMAGE:output_image"
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  output_stream: "SIZE:image_size"
+}
+
+# Calculates rendering scale based on the pose roi.
+node {
+  calculator: "RectToRenderScaleCalculator"
+  input_stream: "NORM_RECT:roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "RENDER_SCALE:render_scale"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderScaleCalculatorOptions] {
+      multiplier: 0.0012
+    }
+  }
+}
+
+# Converts detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTION:detection"
+  output_stream: "RENDER_DATA:detection_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 0 g: 255 b: 0 }
+    }
+  }
+}
+
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "visible_pose_landmarks"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 0 end: 25 }
+    }
+  }
+}
+
+# Converts landmarks to drawing primitives for annotation overlay.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:pose_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_connections: 0
+      landmark_connections: 1
+      landmark_connections: 1
+      landmark_connections: 2
+      landmark_connections: 2
+      landmark_connections: 3
+      landmark_connections: 3
+      landmark_connections: 7
+      landmark_connections: 0
+      landmark_connections: 4
+      landmark_connections: 4
+      landmark_connections: 5
+      landmark_connections: 5
+      landmark_connections: 6
+      landmark_connections: 6
+      landmark_connections: 8
+      landmark_connections: 9
+      landmark_connections: 10
+      landmark_connections: 11
+      landmark_connections: 12
+      landmark_connections: 11
+      landmark_connections: 13
+      landmark_connections: 13
+      landmark_connections: 15
+      landmark_connections: 15
+      landmark_connections: 17
+      landmark_connections: 15
+      landmark_connections: 19
+      landmark_connections: 15
+      landmark_connections: 21
+      landmark_connections: 17
+      landmark_connections: 19
+      landmark_connections: 12
+      landmark_connections: 14
+      landmark_connections: 14
+      landmark_connections: 16
+      landmark_connections: 16
+      landmark_connections: 18
+      landmark_connections: 16
+      landmark_connections: 20
+      landmark_connections: 16
+      landmark_connections: 22
+      landmark_connections: 18
+      landmark_connections: 20
+      landmark_connections: 11
+      landmark_connections: 23
+      landmark_connections: 12
+      landmark_connections: 24
+      landmark_connections: 23
+      landmark_connections: 24
+      landmark_connections: 23
+      landmark_connections: 25
+      landmark_connections: 24
+      landmark_connections: 26
+      landmark_connections: 25
+      landmark_connections: 27
+      landmark_connections: 26
+      landmark_connections: 28
+      landmark_connections: 27
+      landmark_connections: 29
+      landmark_connections: 28
+      landmark_connections: 30
+      landmark_connections: 29
+      landmark_connections: 31
+      landmark_connections: 30
+      landmark_connections: 32
+      landmark_connections: 27
+      landmark_connections: 31
+      landmark_connections: 28
+      landmark_connections: 32
+
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Take left pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "landmarks_left_side"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 1 end: 4 }
+      ranges: { begin: 7 end: 8 }
+      ranges: { begin: 9 end: 10 }
+      ranges: { begin: 11 end: 12 }
+      ranges: { begin: 13 end: 14 }
+      ranges: { begin: 15 end: 16 }
+      ranges: { begin: 17 end: 18 }
+      ranges: { begin: 19 end: 20 }
+      ranges: { begin: 21 end: 22 }
+      ranges: { begin: 23 end: 24 }
+
+      combine_outputs: true
+    }
+  }
+}
+
+# Take right pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "landmarks_right_side"
+  node_options: {
+    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
+      ranges: { begin: 4 end: 7 }
+      ranges: { begin: 8 end: 9 }
+      ranges: { begin: 10 end: 11 }
+      ranges: { begin: 12 end: 13 }
+      ranges: { begin: 14 end: 15 }
+      ranges: { begin: 16 end: 17 }
+      ranges: { begin: 18 end: 19 }
+      ranges: { begin: 20 end: 21 }
+      ranges: { begin: 22 end: 23 }
+      ranges: { begin: 24 end: 25 }
+
+      combine_outputs: true
+    }
+  }
+}
+
+# Render pose joints as big white circles.
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:visible_pose_landmarks"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_background_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 255 b: 255 }
+      connection_color { r: 255 g: 255 b: 255 }
+      thickness: 5.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Render pose left side joints as orange circles (inside white ones).
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_left_side"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_left_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 255 g: 138 b: 0 }
+      connection_color { r: 255 g: 138 b: 0 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Render pose right side joints as cyan circles (inside white ones).
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks_right_side"
+  input_stream: "RENDER_SCALE:render_scale"
+  output_stream: "RENDER_DATA:landmarks_right_joints_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 0 g: 217 b: 231 }
+      connection_color { r: 0 g: 217 b: 231 }
+      thickness: 3.0
+      visualize_landmark_depth: false
+      utilize_visibility: true
+      visibility_threshold: 0.5
+    }
+  }
+}
+
+# Converts normalized rects to drawing primitives for annotation overlay.
+node {
+  calculator: "RectToRenderDataCalculator"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "RENDER_DATA:roi_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
+      filled: false
+      color { r: 255 g: 0 b: 0 }
+      thickness: 4.0
+    }
+  }
+}
+
+# Colors the segmentation mask with the color specified in the option.
+node {
+  calculator: "RecolorCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  input_stream: "MASK_GPU:segmentation_mask"
+  output_stream: "IMAGE_GPU:segmented_image"
+  node_options: {
+    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
+      color { r: 0 g: 0 b: 255 }
+      mask_channel: RED
+      invert_mask: true
+      adjust_with_luminance: false
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:segmented_image"
+  input_stream: "detection_render_data"
+  input_stream: "landmarks_render_data"
+  input_stream: "landmarks_background_joints_render_data"
+  input_stream: "landmarks_left_joints_render_data"
+  input_stream: "landmarks_right_joints_render_data"
+  input_stream: "roi_render_data"
+  output_stream: "IMAGE_GPU:output_image"
+}
diff --git a/mediapipe/graphs/selfie_segmentation/BUILD b/mediapipe/graphs/selfie_segmentation/BUILD
new file mode 100644
index 0000000..ddca178
--- /dev/null
+++ b/mediapipe/graphs/selfie_segmentation/BUILD
@@ -0,0 +1,54 @@
+# Copyright 2021 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "selfie_segmentation_gpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:recolor_calculator",
+        "//mediapipe/modules/selfie_segmentation:selfie_segmentation_gpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "selfie_segmentation_gpu_binary_graph",
+    graph = "selfie_segmentation_gpu.pbtxt",
+    output_name = "selfie_segmentation_gpu.binarypb",
+    deps = [":selfie_segmentation_gpu_deps"],
+)
+
+cc_library(
+    name = "selfie_segmentation_cpu_deps",
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:recolor_calculator",
+        "//mediapipe/modules/selfie_segmentation:selfie_segmentation_cpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "selfie_segmentation_cpu_binary_graph",
+    graph = "selfie_segmentation_cpu.pbtxt",
+    output_name = "selfie_segmentation_cpu.binarypb",
+    deps = [":selfie_segmentation_cpu_deps"],
+)
diff --git a/mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt b/mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt
new file mode 100644
index 0000000..db1b479
--- /dev/null
+++ b/mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt
@@ -0,0 +1,52 @@
+# MediaPipe graph that performs selfie segmentation with TensorFlow Lite on CPU.
+
+# CPU buffer. (ImageFrame)
+input_stream: "input_video"
+
+# Output image with rendered results. (ImageFrame)
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Subgraph that performs selfie segmentation.
+node {
+  calculator: "SelfieSegmentationCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+}
+
+
+# Colors the selfie segmentation with the color specified in the option.
+node {
+  calculator: "RecolorCalculator"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "MASK:segmentation_mask"
+  output_stream: "IMAGE:output_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
+      color { r: 0 g: 0 b: 255 }
+      mask_channel: RED
+      invert_mask: true
+      adjust_with_luminance: false
+    }
+  }
+}
diff --git a/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt b/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt
new file mode 100644
index 0000000..08d4c36
--- /dev/null
+++ b/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt
@@ -0,0 +1,52 @@
+# MediaPipe graph that performs selfie segmentation with TensorFlow Lite on GPU.
+
+# GPU buffer. (GpuBuffer)
+input_stream: "input_video"
+
+# Output image with rendered results. (GpuBuffer)
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Subgraph that performs selfie segmentation.
+node {
+  calculator: "SelfieSegmentationGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+}
+
+
+# Colors the selfie segmentation with the color specified in the option.
+node {
+  calculator: "RecolorCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "MASK_GPU:segmentation_mask"
+  output_stream: "IMAGE_GPU:output_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
+      color { r: 0 g: 0 b: 255 }
+      mask_channel: RED
+      invert_mask: true
+      adjust_with_luminance: false
+    }
+  }
+}
diff --git a/mediapipe/graphs/template_matching/BUILD b/mediapipe/graphs/template_matching/BUILD
new file mode 100644
index 0000000..bc254d2
--- /dev/null
+++ b/mediapipe/graphs/template_matching/BUILD
@@ -0,0 +1,67 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "template_matching_deps",
+    deps = [
+        "//mediapipe/calculators/image:feature_detector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
+        "//mediapipe/calculators/util:timed_box_list_id_to_label_calculator",
+        "//mediapipe/calculators/util:timed_box_list_to_render_data_calculator",
+        "//mediapipe/calculators/video:box_detector_calculator",
+    ],
+)
+
+cc_library(
+    name = "desktop_calculators",
+    deps = [
+        ":template_matching_deps",
+        "//mediapipe/calculators/image:opencv_encoded_image_to_image_frame_calculator",
+        "//mediapipe/calculators/util:local_file_pattern_contents_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+    ],
+)
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        ":template_matching_deps",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "mobile_cpu_binary_graph",
+    graph = "template_matching_mobile_cpu.pbtxt",
+    output_name = "mobile_cpu.binarypb",
+    deps = [":mobile_calculators"],
+)
diff --git a/mediapipe/graphs/template_matching/index_building.pbtxt b/mediapipe/graphs/template_matching/index_building.pbtxt
new file mode 100644
index 0000000..8228139
--- /dev/null
+++ b/mediapipe/graphs/template_matching/index_building.pbtxt
@@ -0,0 +1,92 @@
+# MediaPipe graph that build feature descriptors index for specific target.
+
+# max_queue_size limits the number of packets enqueued on any input stream
+# by throttling inputs to the graph. This makes the graph only process one
+# frame per time.
+max_queue_size: 1
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "LocalFilePatternContentsCalculator"
+  input_side_packet: "FILE_DIRECTORY:file_directory"
+  input_side_packet: "FILE_SUFFIX:file_suffix"
+  output_stream: "CONTENTS:encoded_image"
+}
+
+node {
+  calculator: "OpenCvEncodedImageToImageFrameCalculator"
+  input_stream: "encoded_image"
+  output_stream: "image_frame"
+}
+
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:image_frame"
+  output_stream: "IMAGE:scaled_image_frame"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 320
+      scale_mode: FILL_AND_CROP
+    }
+  }
+}
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:scaled_image_frame"
+  output_stream: "SIZE:input_video_size"
+}
+
+node {
+  calculator: "FeatureDetectorCalculator"
+  input_stream: "IMAGE:scaled_image_frame"
+  output_stream: "FEATURES:features"
+  output_stream: "LANDMARKS:landmarks"
+  output_stream: "PATCHES:patches"
+  node_options: {
+    [type.googleapis.com/mediapipe.FeatureDetectorCalculatorOptions] {
+      max_features: 400
+    }
+  }
+}
+
+# input tensors: 200*32*32*1 float
+# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
+# rest is padded by zero.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:patches"
+  output_stream: "TENSORS:knift_feature_tensors"
+  input_stream_handler {
+    input_stream_handler: "DefaultInputStreamHandler"
+  }
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/knift_float_400.tflite"
+    }
+  }
+}
+
+node {
+  calculator: "TfLiteTensorsToFloatsCalculator"
+  input_stream: "TENSORS:knift_feature_tensors"
+  output_stream: "FLOATS:knift_feature_floats"
+}
+
+node {
+  calculator: "BoxDetectorCalculator"
+  input_side_packet: "OUTPUT_INDEX_FILENAME:output_index_filename"
+  input_stream: "FEATURES:features"
+  input_stream: "IMAGE_SIZE:input_video_size"
+  input_stream: "DESCRIPTORS:knift_feature_floats"
+
+  node_options: {
+    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
+      detector_options {
+        index_type: OPENCV_BF
+        detect_every_n_frame: 1
+      }
+    }
+  }
+}
diff --git a/mediapipe/graphs/template_matching/template_matching_desktop.pbtxt b/mediapipe/graphs/template_matching/template_matching_desktop.pbtxt
new file mode 100644
index 0000000..d44a7e5
--- /dev/null
+++ b/mediapipe/graphs/template_matching/template_matching_desktop.pbtxt
@@ -0,0 +1,141 @@
+# MediaPipe graph that performs object detection on desktop with TensorFlow Lite
+# on CPU.
+# Used in the example in
+# mediapipe/examples/desktop/template_matching:template_matching_tflite
+
+# max_queue_size limits the number of packets enqueued on any input stream
+# by throttling inputs to the graph. This makes the graph only process one
+# frame per time.
+max_queue_size: 1
+
+# Decodes an input video file into images and a video header.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "IMAGE:scaled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 640
+      output_height: 640
+      scale_mode: FILL_AND_CROP
+    }
+  }
+}
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:scaled_input_video"
+  output_stream: "SIZE:input_video_size"
+}
+
+node {
+  calculator: "FeatureDetectorCalculator"
+  input_stream: "IMAGE:scaled_input_video"
+  output_stream: "FEATURES:features"
+  output_stream: "LANDMARKS:landmarks"
+  output_stream: "PATCHES:patches"
+}
+
+# input tensors: 200*32*32*1 float
+# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
+# rest is padded by zero.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:patches"
+  output_stream: "TENSORS:knift_feature_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/knift_float.tflite"
+    }
+  }
+}
+
+node {
+  calculator: "TfLiteTensorsToFloatsCalculator"
+  input_stream: "TENSORS:knift_feature_tensors"
+  output_stream: "FLOATS:knift_feature_floats"
+}
+
+node {
+  calculator: "BoxDetectorCalculator"
+  input_stream: "FEATURES:features"
+  input_stream: "IMAGE_SIZE:input_video_size"
+  input_stream: "DESCRIPTORS:knift_feature_floats"
+  output_stream: "BOXES:detections"
+
+  node_options: {
+    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
+      detector_options {
+        index_type: OPENCV_BF
+        detect_every_n_frame: 1
+      }
+      index_proto_filename: "mediapipe/models/knift_index.pb"
+    }
+  }
+}
+
+node {
+  calculator: "TimedBoxListIdToLabelCalculator"
+  input_stream: "detections"
+  output_stream: "labeled_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TimedBoxListIdToLabelCalculatorOptions] {
+      label_map_path: "mediapipe/models/knift_labelmap.txt"
+    }
+  }
+}
+
+node {
+  calculator: "TimedBoxListToRenderDataCalculator"
+  input_stream: "BOX_LIST:labeled_detections"
+  output_stream: "RENDER_DATA:box_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.TimedBoxListToRenderDataCalculatorOptions] {
+      box_color { r: 255 g: 0 b: 0 }
+      thickness: 5.0
+    }
+  }
+}
+
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  output_stream: "RENDER_DATA:landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 0 g: 255 b: 0 }
+      thickness: 2.0
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_video"
+  input_stream: "box_render_data"
+  input_stream: "landmarks_render_data"
+  output_stream: "IMAGE:output_video"
+}
+
+# Encodes the annotated images into a video file, adopting properties specified
+# in the input video header, e.g., video framerate.
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
diff --git a/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt b/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt
new file mode 100644
index 0000000..e02e12d
--- /dev/null
+++ b/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt
@@ -0,0 +1,137 @@
+# MediaPipe graph that performs template matching with TensorFlow Lite on CPU.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/templatematchingcpu
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Throttles the images flowing downstream for flow control.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Transfers the input image from GPU to CPU memory.
+node: {
+  calculator: "GpuBufferToImageFrameCalculator"
+  input_stream: "throttled_input_video"
+  output_stream: "input_video_cpu"
+}
+
+# Scale the image's longer side to 640, keeping aspect ratio.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:input_video_cpu"
+  output_stream: "IMAGE:transformed_input_video_cpu"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 640
+      output_height: 640
+      scale_mode: FILL_AND_CROP
+    }
+  }
+}
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:transformed_input_video_cpu"
+  output_stream: "SIZE:input_video_size"
+}
+
+node {
+  calculator: "FeatureDetectorCalculator"
+  input_stream: "IMAGE:transformed_input_video_cpu"
+  output_stream: "FEATURES:features"
+  output_stream: "LANDMARKS:landmarks"
+  output_stream: "PATCHES:patches"
+}
+
+# input tensors: 200*32*32*1 float
+# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
+# rest is padded by zero.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:patches"
+  output_stream: "TENSORS:knift_feature_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/knift_float.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+node {
+  calculator: "TfLiteTensorsToFloatsCalculator"
+  input_stream: "TENSORS:knift_feature_tensors"
+  output_stream: "FLOATS:knift_feature_floats"
+}
+
+node {
+  calculator: "BoxDetectorCalculator"
+  input_stream: "FEATURES:features"
+  input_stream: "IMAGE_SIZE:input_video_size"
+  input_stream: "DESCRIPTORS:knift_feature_floats"
+  output_stream: "BOXES:detections"
+
+  node_options: {
+    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
+      detector_options {
+        index_type: OPENCV_BF
+        detect_every_n_frame: 1
+      }
+      index_proto_filename: "mediapipe/models/knift_index.pb"
+    }
+  }
+}
+
+node {
+  calculator: "TimedBoxListIdToLabelCalculator"
+  input_stream: "detections"
+  output_stream: "labeled_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TimedBoxListIdToLabelCalculatorOptions] {
+      label_map_path: "mediapipe/models/knift_labelmap.txt"
+    }
+  }
+}
+
+node {
+  calculator: "TimedBoxListToRenderDataCalculator"
+  input_stream: "BOX_LIST:labeled_detections"
+  output_stream: "RENDER_DATA:box_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.TimedBoxListToRenderDataCalculatorOptions] {
+      box_color { r: 255 g: 0 b: 0 }
+      thickness: 5.0
+    }
+  }
+}
+
+node {
+  calculator: "LandmarksToRenderDataCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  output_stream: "RENDER_DATA:landmarks_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
+      landmark_color { r: 0 g: 255 b: 0 }
+      thickness: 2.0
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:throttled_input_video"
+  input_stream: "box_render_data"
+  input_stream: "landmarks_render_data"
+  output_stream: "IMAGE_GPU:output_video"
+}
diff --git a/mediapipe/graphs/tracking/BUILD b/mediapipe/graphs/tracking/BUILD
new file mode 100644
index 0000000..9e6e75f
--- /dev/null
+++ b/mediapipe/graphs/tracking/BUILD
@@ -0,0 +1,49 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_binary_graph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "mobile_calculators",
+    deps = [
+        "//mediapipe/calculators/core:packet_resampler_calculator",
+        "//mediapipe/graphs/tracking/subgraphs:object_detection_gpu",
+        "//mediapipe/graphs/tracking/subgraphs:object_tracking_gpu",
+        "//mediapipe/graphs/tracking/subgraphs:renderer_gpu",
+    ],
+)
+
+cc_library(
+    name = "desktop_calculators",
+    deps = [
+        "//mediapipe/calculators/core:packet_resampler_calculator",
+        "//mediapipe/graphs/tracking/subgraphs:object_detection_cpu",
+        "//mediapipe/graphs/tracking/subgraphs:object_tracking_cpu",
+        "//mediapipe/graphs/tracking/subgraphs:renderer_cpu",
+    ],
+)
+
+mediapipe_binary_graph(
+    name = "mobile_gpu_binary_graph",
+    graph = "object_detection_tracking_mobile_gpu.pbtxt",
+    output_name = "mobile_gpu.binarypb",
+    deps = [":mobile_calculators"],
+)
diff --git a/mediapipe/graphs/tracking/object_detection_tracking_desktop_live.pbtxt b/mediapipe/graphs/tracking/object_detection_tracking_desktop_live.pbtxt
new file mode 100644
index 0000000..4b21ee5
--- /dev/null
+++ b/mediapipe/graphs/tracking/object_detection_tracking_desktop_live.pbtxt
@@ -0,0 +1,45 @@
+# MediaPipe graph that performs object detection and tracking with TensorFlow
+# Lite on CPU.
+# Used in the examples in
+# mediapipie/examples/desktop/object_tracking/
+
+# Images on CPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Resamples the images by specific frame rate. This calculator is used to
+# control the frequecy of subsequent calculators/subgraphs, e.g. less power
+# consumption for expensive process.
+node {
+  calculator: "PacketResamplerCalculator"
+  input_stream: "DATA:input_video"
+  output_stream: "DATA:throttled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.PacketResamplerCalculatorOptions] {
+      frame_rate: 3
+    }
+  }
+}
+
+# Subgraph that detections objects (see object_detection_cpu.pbtxt).
+node {
+  calculator: "ObjectDetectionSubgraphCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:output_detections"
+}
+
+# Subgraph that tracks objects (see object_tracking_cpu.pbtxt).
+node {
+  calculator: "ObjectTrackingSubgraphCpu"
+  input_stream: "VIDEO:input_video"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "DETECTIONS:tracked_detections"
+}
+
+# Subgraph that renders annotations and overlays them on top of input images (see renderer_cpu.pbtxt).
+node {
+  calculator: "RendererSubgraphCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "DETECTIONS:tracked_detections"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/tracking/object_detection_tracking_mobile_gpu.pbtxt b/mediapipe/graphs/tracking/object_detection_tracking_mobile_gpu.pbtxt
new file mode 100644
index 0000000..0ef9830
--- /dev/null
+++ b/mediapipe/graphs/tracking/object_detection_tracking_mobile_gpu.pbtxt
@@ -0,0 +1,46 @@
+# MediaPipe graph that performs object detection and tracking with TensorFlow
+# Lite on GPU.
+# Used in the examples in
+# mediapipie/examples/android/src/java/com/mediapipe/apps/objecttrackinggpu
+
+# Images on GPU coming into and out of the graph.
+input_stream: "input_video"
+output_stream: "output_video"
+
+# Resamples the images by specific frame rate. This calculator is used to
+# control the frequecy of subsequent calculators/subgraphs, e.g. less power
+# consumption for expensive process.
+node {
+  calculator: "PacketResamplerCalculator"
+  input_stream: "DATA:input_video"
+  output_stream: "DATA:throttled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.PacketResamplerCalculatorOptions] {
+      frame_rate: 0.5
+    }
+  }
+}
+
+# Subgraph that detections objects (see object_detection_gpu.pbtxt).
+node {
+  calculator: "ObjectDetectionSubgraphGpu"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "DETECTIONS:output_detections"
+}
+
+# Subgraph that tracks objects (see object_tracking_gpu.pbtxt).
+node {
+  calculator: "ObjectTrackingSubgraphGpu"
+  input_stream: "VIDEO:input_video"
+  input_stream: "DETECTIONS:output_detections"
+  output_stream: "DETECTIONS:tracked_detections"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see renderer_gpu.pbtxt).
+node {
+  calculator: "RendererSubgraphGpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "DETECTIONS:tracked_detections"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/BUILD b/mediapipe/graphs/tracking/subgraphs/BUILD
new file mode 100644
index 0000000..16f87f3
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/BUILD
@@ -0,0 +1,129 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "box_tracking_gpu",
+    graph = "box_tracking_gpu.pbtxt",
+    register_as = "BoxTrackingSubgraphGpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/video:box_tracker_calculator",
+        "//mediapipe/calculators/video:flow_packager_calculator",
+        "//mediapipe/calculators/video:motion_analysis_calculator",
+        "//mediapipe/framework/stream_handler:immediate_input_stream_handler",
+        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
+        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "box_tracking_cpu",
+    graph = "box_tracking_cpu.pbtxt",
+    register_as = "BoxTrackingSubgraphCpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/video:box_tracker_calculator",
+        "//mediapipe/calculators/video:flow_packager_calculator",
+        "//mediapipe/calculators/video:motion_analysis_calculator",
+        "//mediapipe/framework/stream_handler:immediate_input_stream_handler",
+        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "object_tracking_gpu",
+    graph = "object_tracking_gpu.pbtxt",
+    register_as = "ObjectTrackingSubgraphGpu",
+    deps = [
+        "//mediapipe/calculators/util:detection_unique_id_calculator",
+        "//mediapipe/calculators/util:detections_to_timed_box_list_calculator",
+        "//mediapipe/calculators/video:tracked_detection_manager_calculator",
+        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
+        "//mediapipe/graphs/tracking/subgraphs:box_tracking_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "object_tracking_cpu",
+    graph = "object_tracking_cpu.pbtxt",
+    register_as = "ObjectTrackingSubgraphCpu",
+    deps = [
+        "//mediapipe/calculators/util:detection_unique_id_calculator",
+        "//mediapipe/calculators/util:detections_to_timed_box_list_calculator",
+        "//mediapipe/calculators/video:tracked_detection_manager_calculator",
+        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
+        "//mediapipe/graphs/tracking/subgraphs:box_tracking_cpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "object_detection_gpu",
+    graph = "object_detection_gpu.pbtxt",
+    register_as = "ObjectDetectionSubgraphGpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "object_detection_cpu",
+    graph = "object_detection_cpu.pbtxt",
+    register_as = "ObjectDetectionSubgraphCpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "renderer_gpu",
+    graph = "renderer_gpu.pbtxt",
+    register_as = "RendererSubgraphGpu",
+    deps = [
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "renderer_cpu",
+    graph = "renderer_cpu.pbtxt",
+    register_as = "RendererSubgraphCpu",
+    deps = [
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:detections_to_render_data_calculator",
+        "//mediapipe/calculators/util:rect_to_render_data_calculator",
+    ],
+)
diff --git a/mediapipe/graphs/tracking/subgraphs/box_tracking_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/box_tracking_cpu.pbtxt
new file mode 100644
index 0000000..b8c4e2f
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/box_tracking_cpu.pbtxt
@@ -0,0 +1,119 @@
+# MediaPipe box tracking subgraph.
+
+type: "BoxTrackingSubgraphCpu"
+
+input_stream: "VIDEO:input_video"
+input_stream: "BOXES:start_pos"
+input_stream: "CANCEL_ID:cancel_object_id"
+output_stream: "BOXES:boxes"
+
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "IMAGE:downscaled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 240
+    }
+  }
+}
+
+# Performs motion analysis on an incoming video stream.
+node: {
+  calculator: "MotionAnalysisCalculator"
+  input_stream: "VIDEO:downscaled_input_video"
+  output_stream: "CAMERA:camera_motion"
+  output_stream: "FLOW:region_flow"
+
+  node_options: {
+    [type.googleapis.com/mediapipe.MotionAnalysisCalculatorOptions]: {
+      analysis_options {
+        analysis_policy: ANALYSIS_POLICY_CAMERA_MOBILE
+        flow_options {
+          fast_estimation_min_block_size: 100
+          top_inlier_sets: 1
+          frac_inlier_error_threshold: 3e-3
+          downsample_mode: DOWNSAMPLE_TO_INPUT_SIZE
+          verification_distance: 5.0
+          verify_long_feature_acceleration: true
+          verify_long_feature_trigger_ratio: 0.1
+          tracking_options {
+            max_features: 500
+            adaptive_extraction_levels: 2
+            min_eig_val_settings {
+              adaptive_lowest_quality_level: 2e-4
+            }
+            klt_tracker_implementation: KLT_OPENCV
+          }
+        }
+      }
+    }
+  }
+}
+
+# Reads optical flow fields defined in
+# mediapipe/framework/formats/motion/optical_flow_field.h,
+# returns a VideoFrame with 2 channels (v_x and v_y), each channel is quantized
+# to 0-255.
+node: {
+  calculator: "FlowPackagerCalculator"
+  input_stream: "FLOW:region_flow"
+  input_stream: "CAMERA:camera_motion"
+  output_stream: "TRACKING:tracking_data"
+
+  node_options: {
+    [type.googleapis.com/mediapipe.FlowPackagerCalculatorOptions]: {
+      flow_packager_options: {
+        binary_tracking_data_support: false
+      }
+    }
+  }
+}
+
+# Tracks box positions over time.
+node: {
+  calculator: "BoxTrackerCalculator"
+  input_stream: "TRACKING:tracking_data"
+  input_stream: "TRACK_TIME:input_video"
+  input_stream: "START_POS:start_pos"
+  input_stream: "CANCEL_OBJECT_ID:cancel_object_id"
+  input_stream_info: {
+    tag_index: "CANCEL_OBJECT_ID"
+    back_edge: true
+  }
+  output_stream: "BOXES:boxes"
+
+  input_stream_handler {
+    input_stream_handler: "SyncSetInputStreamHandler"
+    options {
+      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
+        sync_set {
+          tag_index: "TRACKING"
+          tag_index: "TRACK_TIME"
+        }
+        sync_set {
+          tag_index: "START_POS"
+        }
+        sync_set {
+          tag_index: "CANCEL_OBJECT_ID"
+        }
+      }
+    }
+  }
+
+  node_options: {
+    [type.googleapis.com/mediapipe.BoxTrackerCalculatorOptions]: {
+      tracker_options: {
+        track_step_options {
+          track_object_and_camera: true
+          tracking_degrees: TRACKING_DEGREE_OBJECT_SCALE
+          inlier_spring_force: 0.0
+          static_motion_temporal_ratio: 3e-2
+        }
+      }
+      visualize_tracking_data: false
+      streaming_track_data_cache_size: 100
+    }
+  }
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/box_tracking_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/box_tracking_gpu.pbtxt
new file mode 100644
index 0000000..cab2b77
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/box_tracking_gpu.pbtxt
@@ -0,0 +1,126 @@
+# MediaPipe box tracking subgraph.
+
+type: "BoxTrackingSubgraphGpu"
+
+input_stream: "VIDEO:input_video"
+input_stream: "BOXES:start_pos"
+input_stream: "CANCEL_ID:cancel_object_id"
+output_stream: "BOXES:boxes"
+
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "IMAGE_GPU:downscaled_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 240
+      output_height: 320
+    }
+  }
+}
+
+# Converts GPU buffer to ImageFrame for processing tracking.
+node: {
+  calculator: "GpuBufferToImageFrameCalculator"
+  input_stream: "downscaled_input_video"
+  output_stream: "downscaled_input_video_cpu"
+}
+
+# Performs motion analysis on an incoming video stream.
+node: {
+  calculator: "MotionAnalysisCalculator"
+  input_stream: "VIDEO:downscaled_input_video_cpu"
+  output_stream: "CAMERA:camera_motion"
+  output_stream: "FLOW:region_flow"
+
+  node_options: {
+    [type.googleapis.com/mediapipe.MotionAnalysisCalculatorOptions]: {
+      analysis_options {
+        analysis_policy: ANALYSIS_POLICY_CAMERA_MOBILE
+        flow_options {
+          fast_estimation_min_block_size: 100
+          top_inlier_sets: 1
+          frac_inlier_error_threshold: 3e-3
+          downsample_mode: DOWNSAMPLE_TO_INPUT_SIZE
+          verification_distance: 5.0
+          verify_long_feature_acceleration: true
+          verify_long_feature_trigger_ratio: 0.1
+          tracking_options {
+            max_features: 500
+            adaptive_extraction_levels: 2
+            min_eig_val_settings {
+              adaptive_lowest_quality_level: 2e-4
+            }
+            klt_tracker_implementation: KLT_OPENCV
+          }
+        }
+      }
+    }
+  }
+}
+
+# Reads optical flow fields defined in
+# mediapipe/framework/formats/motion/optical_flow_field.h,
+# returns a VideoFrame with 2 channels (v_x and v_y), each channel is quantized
+# to 0-255.
+node: {
+  calculator: "FlowPackagerCalculator"
+  input_stream: "FLOW:region_flow"
+  input_stream: "CAMERA:camera_motion"
+  output_stream: "TRACKING:tracking_data"
+
+  node_options: {
+    [type.googleapis.com/mediapipe.FlowPackagerCalculatorOptions]: {
+      flow_packager_options: {
+        binary_tracking_data_support: false
+      }
+    }
+  }
+}
+
+# Tracks box positions over time.
+node: {
+  calculator: "BoxTrackerCalculator"
+  input_stream: "TRACKING:tracking_data"
+  input_stream: "TRACK_TIME:input_video"
+  input_stream: "START_POS:start_pos"
+  input_stream: "CANCEL_OBJECT_ID:cancel_object_id"
+  input_stream_info: {
+    tag_index: "CANCEL_OBJECT_ID"
+    back_edge: true
+  }
+  output_stream: "BOXES:boxes"
+
+  input_stream_handler {
+    input_stream_handler: "SyncSetInputStreamHandler"
+    options {
+      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
+        sync_set {
+          tag_index: "TRACKING"
+          tag_index: "TRACK_TIME"
+        }
+        sync_set {
+          tag_index: "START_POS"
+        }
+        sync_set {
+          tag_index: "CANCEL_OBJECT_ID"
+        }
+      }
+    }
+  }
+
+  node_options: {
+    [type.googleapis.com/mediapipe.BoxTrackerCalculatorOptions]: {
+      tracker_options: {
+        track_step_options {
+          track_object_and_camera: true
+          tracking_degrees: TRACKING_DEGREE_OBJECT_SCALE
+          inlier_spring_force: 0.0
+          static_motion_temporal_ratio: 3e-2
+        }
+      }
+      visualize_tracking_data: false
+      streaming_track_data_cache_size: 100
+    }
+  }
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_detection_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_detection_cpu.pbtxt
new file mode 100644
index 0000000..54d6af3
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/object_detection_cpu.pbtxt
@@ -0,0 +1,128 @@
+# MediaPipe object detection subgraph.
+
+type: "ObjectDetectionSubgraphCpu"
+
+input_stream: "IMAGE:input_video"
+output_stream: "DETECTIONS:output_detections"
+
+# Transforms the input image on CPU to a 320x320 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the object
+# detection model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "IMAGE:transformed_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 320
+    }
+  }
+}
+
+# Converts the transformed input image on CPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE:transformed_input_video"
+  output_stream: "TENSORS:image_tensor"
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  node_options: {
+    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 320
+      input_size_width: 320
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TfLiteTensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
+      num_classes: 91
+      num_boxes: 2034
+      num_coords: 4
+      ignore_classes: 0
+      sigmoid_score: true
+      apply_exponential_on_box_size: true
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+      min_score_thresh: 0.6
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "detections"
+  output_stream: "filtered_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
+      min_suppression_threshold: 0.4
+      max_num_detections: 3
+      overlap_type: INTERSECTION_OVER_UNION
+      return_empty_detections: true
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "output_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
+    }
+  }
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_detection_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_detection_gpu.pbtxt
new file mode 100644
index 0000000..f3cc2c8
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/object_detection_gpu.pbtxt
@@ -0,0 +1,128 @@
+# MediaPipe object detection subgraph.
+
+type: "ObjectDetectionSubgraphGpu"
+
+input_stream: "IMAGE:input_video"
+output_stream: "DETECTIONS:output_detections"
+
+# Transforms the input image on GPU to a 320x320 image. To scale the image, by
+# default it uses the STRETCH scale mode that maps the entire input image to the
+# entire transformed image. As a result, image aspect ratio may be changed and
+# objects in the image may be deformed (stretched or squeezed), but the object
+# detection model used in this graph is agnostic to that deformation.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "IMAGE_GPU:transformed_input_video"
+  node_options: {
+    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
+      output_width: 320
+      output_height: 320
+    }
+  }
+}
+
+# Converts the transformed input image on GPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE_GPU:transformed_input_video"
+  output_stream: "TENSORS_GPU:image_tensor"
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS_GPU:image_tensor"
+  output_stream: "TENSORS_GPU:detection_tensors"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  node_options: {
+    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 320
+      input_size_width: 320
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TfLiteTensorsToDetectionsCalculator"
+  input_stream: "TENSORS_GPU:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
+      num_classes: 91
+      num_boxes: 2034
+      num_coords: 4
+      ignore_classes: 0
+      sigmoid_score: true
+      apply_exponential_on_box_size: true
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+      min_score_thresh: 0.6
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "detections"
+  output_stream: "filtered_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
+      min_suppression_threshold: 0.4
+      max_num_detections: 3
+      overlap_type: INTERSECTION_OVER_UNION
+      return_empty_detections: true
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "output_detections"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
+      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
+    }
+  }
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_tracking_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_tracking_cpu.pbtxt
new file mode 100644
index 0000000..9ac7978
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/object_tracking_cpu.pbtxt
@@ -0,0 +1,56 @@
+# MediaPipe object tracking subgraph.
+
+type: "ObjectTrackingSubgraphCpu"
+
+input_stream: "VIDEO:input_video"
+input_stream: "DETECTIONS:new_detections"
+output_stream: "DETECTIONS:tracked_detections"
+
+# Assigns an unique id for each new detection.
+node {
+  calculator: "DetectionUniqueIdCalculator"
+  input_stream: "DETECTIONS:new_detections"
+  output_stream: "DETECTIONS:detections_with_id"
+}
+
+# Converts detections to TimedBox protos which are used as initial location
+# for tracking.
+node {
+  calculator: "DetectionsToTimedBoxListCalculator"
+  input_stream: "DETECTIONS:detections_with_id"
+  output_stream: "BOXES:start_pos"
+}
+
+# Subgraph that tracks boxes (see box_tracking_cpu.pbtxt).
+node {
+  calculator: "BoxTrackingSubgraphCpu"
+  input_stream: "VIDEO:input_video"
+  input_stream: "BOXES:start_pos"
+  input_stream: "CANCEL_ID:cancel_object_id"
+  output_stream: "BOXES:boxes"
+}
+
+# Managers new detected objects and objects that are being tracked.
+# It associates the duplicated detections and updates the locations of
+# detections from tracking.
+node: {
+  calculator: "TrackedDetectionManagerCalculator"
+  input_stream: "DETECTIONS:detections_with_id"
+  input_stream: "TRACKING_BOXES:boxes"
+  output_stream: "DETECTIONS:tracked_detections"
+  output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
+
+  input_stream_handler {
+    input_stream_handler: "SyncSetInputStreamHandler"
+    options {
+      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
+        sync_set {
+          tag_index: "TRACKING_BOXES"
+        }
+        sync_set {
+          tag_index: "DETECTIONS"
+        }
+      }
+    }
+  }
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_tracking_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_tracking_gpu.pbtxt
new file mode 100644
index 0000000..ab27dbd
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/object_tracking_gpu.pbtxt
@@ -0,0 +1,56 @@
+# MediaPipe object tracking subgraph.
+
+type: "ObjectTrackingSubgraphGpu"
+
+input_stream: "VIDEO:input_video"
+input_stream: "DETECTIONS:new_detections"
+output_stream: "DETECTIONS:tracked_detections"
+
+# Assigns an unique id for each new detection.
+node {
+  calculator: "DetectionUniqueIdCalculator"
+  input_stream: "DETECTIONS:new_detections"
+  output_stream: "DETECTIONS:detections_with_id"
+}
+
+# Converts detections to TimedBox protos which are used as initial location
+# for tracking.
+node {
+  calculator: "DetectionsToTimedBoxListCalculator"
+  input_stream: "DETECTIONS:detections_with_id"
+  output_stream: "BOXES:start_pos"
+}
+
+# Subgraph that tracks boxes (see box_tracking_gpu.pbtxt).
+node {
+  calculator: "BoxTrackingSubgraphGpu"
+  input_stream: "VIDEO:input_video"
+  input_stream: "BOXES:start_pos"
+  input_stream: "CANCEL_ID:cancel_object_id"
+  output_stream: "BOXES:boxes"
+}
+
+# Managers new detected objects and objects that are being tracked.
+# It associates the duplicated detections and updates the locations of
+# detections from tracking.
+node: {
+  calculator: "TrackedDetectionManagerCalculator"
+  input_stream: "DETECTIONS:detections_with_id"
+  input_stream: "TRACKING_BOXES:boxes"
+  output_stream: "DETECTIONS:tracked_detections"
+  output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
+
+  input_stream_handler {
+    input_stream_handler: "SyncSetInputStreamHandler"
+    options {
+      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
+        sync_set {
+          tag_index: "TRACKING_BOXES"
+        }
+        sync_set {
+          tag_index: "DETECTIONS"
+        }
+      }
+    }
+  }
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/renderer_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/renderer_cpu.pbtxt
new file mode 100644
index 0000000..665126a
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/renderer_cpu.pbtxt
@@ -0,0 +1,29 @@
+# MediaPipe object tracking rendering subgraph.
+
+type: "RendererSubgraphCpu"
+
+input_stream: "IMAGE:input_image"
+input_stream: "DETECTIONS:detections"
+output_stream: "IMAGE:output_image"
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:detections_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+      render_detection_id: true
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_image"
+  input_stream: "detections_render_data"
+  output_stream: "IMAGE:output_image"
+}
diff --git a/mediapipe/graphs/tracking/subgraphs/renderer_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/renderer_gpu.pbtxt
new file mode 100644
index 0000000..e94fb6d
--- /dev/null
+++ b/mediapipe/graphs/tracking/subgraphs/renderer_gpu.pbtxt
@@ -0,0 +1,29 @@
+# MediaPipe object tracking rendering subgraph.
+
+type: "RendererSubgraphGpu"
+
+input_stream: "IMAGE:input_image"
+input_stream: "DETECTIONS:detections"
+output_stream: "IMAGE:output_image"
+
+# Converts the detections to drawing primitives for annotation overlay.
+node {
+  calculator: "DetectionsToRenderDataCalculator"
+  input_stream: "DETECTIONS:detections"
+  output_stream: "RENDER_DATA:detections_render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
+      thickness: 4.0
+      color { r: 255 g: 0 b: 0 }
+      render_detection_id: true
+    }
+  }
+}
+
+# Draws annotations and overlays them on top of the input images.
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE_GPU:input_image"
+  input_stream: "detections_render_data"
+  output_stream: "IMAGE_GPU:output_image"
+}
diff --git a/mediapipe/graphs/youtube8m/BUILD b/mediapipe/graphs/youtube8m/BUILD
new file mode 100644
index 0000000..7318a8c
--- /dev/null
+++ b/mediapipe/graphs/youtube8m/BUILD
@@ -0,0 +1,73 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "yt8m_feature_extraction_calculators",
+    deps = [
+        "//mediapipe/calculators/audio:audio_decoder_calculator",
+        "//mediapipe/calculators/audio:basic_time_series_calculators",
+        "//mediapipe/calculators/audio:mfcc_mel_calculators",
+        "//mediapipe/calculators/audio:rational_factor_resample_calculator",
+        "//mediapipe/calculators/audio:spectrogram_calculator",
+        "//mediapipe/calculators/audio:stabilized_log_calculator",
+        "//mediapipe/calculators/audio:time_series_framer_calculator",
+        "//mediapipe/calculators/core:add_header_calculator",
+        "//mediapipe/calculators/core:matrix_multiply_calculator",
+        "//mediapipe/calculators/core:matrix_subtract_calculator",
+        "//mediapipe/calculators/core:matrix_to_vector_calculator",
+        "//mediapipe/calculators/core:packet_cloner_calculator",
+        "//mediapipe/calculators/core:packet_resampler_calculator",
+        "//mediapipe/calculators/tensorflow:image_frame_to_tensor_calculator",
+        "//mediapipe/calculators/tensorflow:matrix_to_tensor_calculator",
+        "//mediapipe/calculators/tensorflow:pack_media_sequence_calculator",
+        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
+        "//mediapipe/calculators/tensorflow:tensor_squeeze_dimensions_calculator",
+        "//mediapipe/calculators/tensorflow:tensor_to_matrix_calculator",
+        "//mediapipe/calculators/tensorflow:tensorflow_inference_calculator",
+        "//mediapipe/calculators/tensorflow:tensorflow_session_from_frozen_graph_calculator",
+        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+    ],
+)
+
+cc_library(
+    name = "yt8m_inference_calculators_deps",
+    deps = [
+        "//mediapipe/calculators/core:concatenate_vector_calculator",
+        "//mediapipe/calculators/core:dequantize_byte_array_calculator",
+        "//mediapipe/calculators/core:packet_cloner_calculator",
+        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
+        "//mediapipe/calculators/core:string_to_int_calculator",
+        "//mediapipe/calculators/tensorflow:lapped_tensor_buffer_calculator",
+        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
+        "//mediapipe/calculators/tensorflow:tensor_to_vector_float_calculator",
+        "//mediapipe/calculators/tensorflow:tensorflow_inference_calculator",
+        "//mediapipe/calculators/tensorflow:tensorflow_session_from_saved_model_calculator",
+        "//mediapipe/calculators/tensorflow:tfrecord_reader_calculator",
+        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
+        "//mediapipe/calculators/tensorflow:unpack_yt8m_sequence_example_calculator",
+        "//mediapipe/calculators/tensorflow:vector_float_to_tensor_calculator",
+        "//mediapipe/calculators/tensorflow:vector_int_to_tensor_calculator",
+        "//mediapipe/calculators/util:annotation_overlay_calculator",
+        "//mediapipe/calculators/util:labels_to_render_data_calculator",
+        "//mediapipe/calculators/util:local_file_contents_calculator",
+        "//mediapipe/calculators/util:top_k_scores_calculator",
+        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
+        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
+    ],
+)
diff --git a/mediapipe/graphs/youtube8m/feature_extraction.pbtxt b/mediapipe/graphs/youtube8m/feature_extraction.pbtxt
new file mode 100644
index 0000000..89d1053
--- /dev/null
+++ b/mediapipe/graphs/youtube8m/feature_extraction.pbtxt
@@ -0,0 +1,295 @@
+input_side_packet: "input_sequence_example"
+input_side_packet: "inception3_pca_mean_matrix"
+input_side_packet: "inception3_pca_projection_matrix"
+input_side_packet: "vggish_pca_mean_matrix"
+input_side_packet: "vggish_pca_projection_matrix"
+output_side_packet: "sequence_example_to_serialize"
+
+node {
+  calculator: "StringToSequenceExampleCalculator"
+  input_side_packet: "STRING:input_sequence_example"
+  output_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+}
+
+node {
+  calculator: "UnpackMediaSequenceCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+  output_side_packet: "DATA_PATH:input_file"
+  output_side_packet: "RESAMPLER_OPTIONS:packet_resampler_options"
+  output_side_packet: "AUDIO_DECODER_OPTIONS:audio_decoder_options"
+  node_options: {
+    [type.googleapis.com/mediapipe.UnpackMediaSequenceCalculatorOptions]: {
+      base_packet_resampler_options {
+        frame_rate: 1.0
+        base_timestamp: 0
+      }
+      base_audio_decoder_options {
+        audio_stream { stream_index: 0 }
+      }
+    }
+  }
+}
+
+# Decode the entire video.
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_file"
+  output_stream: "VIDEO:decoded_frames"
+}
+
+# Extract the subset of frames we want to keep.
+node {
+  calculator: "PacketResamplerCalculator"
+  input_stream: "decoded_frames"
+  output_stream: "sampled_decoded_frames"
+  input_side_packet: "OPTIONS:packet_resampler_options"
+}
+
+node {
+  calculator: "ImageFrameToTensorCalculator"
+  input_stream: "sampled_decoded_frames"
+  output_stream: "tensor_frame"
+}
+
+node {
+  calculator: "TensorFlowSessionFromFrozenGraphCalculator"
+  output_side_packet: "SESSION:session"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowSessionFromFrozenGraphCalculatorOptions]: {
+      graph_proto_path: "/tmp/mediapipe/classify_image_graph_def.pb"
+      tag_to_tensor_names {
+        key: "IMG_UINT8"
+        value: "DecodeJpeg:0"
+      }
+      tag_to_tensor_names {
+        key: "INCEPTION_POOL3"
+        value: "pool_3/_reshape:0"
+      }
+    }
+  }
+}
+
+node {
+  calculator: "TensorFlowInferenceCalculator"
+  input_side_packet: "SESSION:session"
+  input_stream: "IMG_UINT8:tensor_frame"
+  output_stream: "INCEPTION_POOL3:inception3_hidden_activation_single_element_batch"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
+      signature_name: ""
+      batch_size: 1
+      add_batch_dim_to_tensors: false
+    }
+  }
+}
+
+# Remove the batch dimension.
+node: {
+  calculator: "TensorSqueezeDimensionsCalculator"
+  input_stream: "inception3_hidden_activation_single_element_batch"
+  output_stream: "inception3_hidden_activation"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorSqueezeDimensionsCalculatorOptions]: {
+      dim: 0
+    }
+  }
+}
+
+node {
+  calculator: "TensorToMatrixCalculator"
+  input_stream: "TENSOR:inception3_hidden_activation"
+  output_stream: "MATRIX:inception3_hidden_activation_matrix"
+}
+
+node {
+  calculator: "MatrixSubtractCalculator"
+  input_stream: "MINUEND:inception3_hidden_activation_matrix"
+  input_side_packet: "SUBTRAHEND:inception3_pca_mean_matrix"
+  output_stream: "mean_subtracted_inception3_matrix"
+}
+node {
+  calculator: "MatrixMultiplyCalculator"
+  input_stream: "mean_subtracted_inception3_matrix"
+  input_side_packet: "inception3_pca_projection_matrix"
+  output_stream: "pca_inception3_matrix"
+}
+node {
+  calculator: "MatrixToVectorCalculator"
+  input_stream: "pca_inception3_matrix"
+  output_stream: "pca_inception3_vf"
+}
+
+######################## END OF VISUAL ###########################
+
+######################## BEGIN OF AUDIO ##########################
+node {
+  calculator: "AudioDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_file"
+  input_side_packet: "OPTIONS:audio_decoder_options"
+  output_stream: "AUDIO:audio"
+  output_stream: "AUDIO_HEADER:audio_header"
+}
+
+node {
+  calculator: "AddHeaderCalculator"
+  input_stream: "DATA:audio"
+  input_stream: "HEADER:audio_header"
+  output_stream: "media_audio"
+}
+
+# Always convert the audio to mono.
+node {
+  calculator: "AverageTimeSeriesAcrossChannelsCalculator"
+  input_stream: "media_audio"
+  output_stream: "mono_waveform"
+}
+
+node {
+  calculator: "RationalFactorResampleCalculator"
+  input_stream: "mono_waveform"
+  output_stream: "resampled_waveform"
+  node_options: {
+    [type.googleapis.com/mediapipe.RationalFactorResampleCalculatorOptions] {
+      target_sample_rate: 16000.0
+    }
+  }
+}
+node {
+  calculator: "SpectrogramCalculator"
+  input_stream: "resampled_waveform"
+  output_stream: "spectrogram_squared_magnitude"
+  node_options: {
+    [type.googleapis.com/mediapipe.SpectrogramCalculatorOptions] {
+      frame_duration_seconds: 0.025
+      frame_overlap_seconds: 0.015
+      output_type: SQUARED_MAGNITUDE
+    }
+  }
+}
+node {
+  calculator: "MelSpectrumCalculator"
+  # MelSpectrumCalculator expects SQUARED_MAGNITUDE input, but its output is in
+  # linear magnitude units.
+  input_stream: "spectrogram_squared_magnitude"
+  output_stream: "mel_spectrum_magnitude"
+  node_options: {
+    [type.googleapis.com/mediapipe.MelSpectrumCalculatorOptions] {
+      # Follow the 'wideband' or '16kHz' speech convention.
+      channel_count: 64
+      min_frequency_hertz: 125.0
+      max_frequency_hertz: 7500.0
+    }
+  }
+}
+node {
+  calculator: "StabilizedLogCalculator"
+  input_stream: "mel_spectrum_magnitude"
+  output_stream: "log_mel_spectrum_magnitude"
+  node_options: {
+    [type.googleapis.com/mediapipe.StabilizedLogCalculatorOptions] {
+      stabilizer: 0.01
+    }
+  }
+}
+node {
+  calculator: "TimeSeriesFramerCalculator"
+  input_stream: "log_mel_spectrum_magnitude"
+  output_stream: "log_mel_spectrum_magnitude_with_context"
+  node_options: {
+    [type.googleapis.com/mediapipe.TimeSeriesFramerCalculatorOptions] {
+      frame_duration_seconds: 0.96
+      frame_overlap_seconds: -0.04
+    }
+  }
+}
+node {
+  calculator: "MatrixToTensorCalculator"
+  input_stream: "log_mel_spectrum_magnitude_with_context"
+  output_stream: "log_mel_spectrum_magnitude_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.MatrixToTensorCalculatorOptions] {
+      transpose: true
+    }
+  }
+}
+
+node {
+  calculator: "TensorFlowSessionFromFrozenGraphCalculator"
+  output_side_packet: "SESSION:vggish_session"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowSessionFromFrozenGraphCalculatorOptions]: {
+      graph_proto_path: "/tmp/mediapipe/vggish_new.pb"
+      tag_to_tensor_names {
+        key: "INPUT"
+        value: "vggish/input_features:0"
+      }
+      tag_to_tensor_names {
+        key: "VGGISH"
+        value: "vggish/fc2/BiasAdd:0"
+      }
+    }
+  }
+}
+
+node {
+  calculator: "TensorFlowInferenceCalculator"
+  input_side_packet: "SESSION:vggish_session"
+  input_stream: "INPUT:log_mel_spectrum_magnitude_tensor"
+  output_stream: "VGGISH:vggish_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
+      signature_name: ""
+      batch_size: 128
+    }
+  }
+}
+
+node {
+  calculator: "TensorToMatrixCalculator"
+  input_stream: "REFERENCE:log_mel_spectrum_magnitude_with_context"
+  input_stream: "TENSOR:vggish_tensor"
+  output_stream: "MATRIX:vggish_matrix"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorToMatrixCalculatorOptions] {
+      time_series_header_overrides {
+        num_channels: 128
+        num_samples: 1
+      }
+    }
+  }
+}
+
+node {
+  calculator: "MatrixSubtractCalculator"
+  input_stream: "MINUEND:vggish_matrix"
+  input_side_packet: "SUBTRAHEND:vggish_pca_mean_matrix"
+  output_stream: "mean_subtracted_vggish_matrix"
+}
+node {
+  calculator: "MatrixMultiplyCalculator"
+  input_stream: "mean_subtracted_vggish_matrix"
+  input_side_packet: "vggish_pca_projection_matrix"
+  output_stream: "pca_vggish_matrix"
+}
+node {
+  calculator: "MatrixToVectorCalculator"
+  input_stream: "pca_vggish_matrix"
+  output_stream: "pca_vggish_vf"
+}
+
+# Store the features in the SequenceExample.
+node {
+  calculator: "PackMediaSequenceCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+  output_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
+  input_stream: "FLOAT_FEATURE_RGB:pca_inception3_vf"
+  input_stream: "FLOAT_FEATURE_AUDIO:pca_vggish_vf"
+}
+
+# Serialize the SequenceExample to a string for storage.
+node {
+  calculator: "StringToSequenceExampleCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
+  output_side_packet: "STRING:output_sequence_example"
+}
+
diff --git a/mediapipe/graphs/youtube8m/label_map.txt b/mediapipe/graphs/youtube8m/label_map.txt
new file mode 100644
index 0000000..a3ed470
--- /dev/null
+++ b/mediapipe/graphs/youtube8m/label_map.txt
@@ -0,0 +1,3862 @@
+Game
+Video game
+Vehicle
+Concert
+Musician
+Cartoon
+Performance art
+Car
+Dance
+Guitar
+String instrument
+Food
+Association football
+Musical ensemble
+Music video
+Animal
+Animation
+Motorsport
+Pet
+Racing
+Recipe
+Mobile phone
+Cooking
+Smartphone
+Gadget
+Trailer (promotion)
+Toy
+Minecraft
+Drum kit
+Cuisine
+Motorcycle
+Piano
+Dish (food)
+Drum
+Acoustic guitar
+Action-adventure game
+Call of Duty
+Electric guitar
+Drummer
+Cosmetics
+Keyboard instrument
+Choir
+Strategy video game
+Fishing
+Aircraft
+Train
+Airplane
+Pianist
+Sports car
+Art
+Hair
+Rail transport
+Basketball
+Cycling
+Orchestra
+Motorcycling
+Transport
+Musical keyboard
+Bicycle
+Fish
+Outdoor recreation
+Disc jockey
+Machine
+Sports game
+Radio-controlled model
+Hairstyle
+Fashion
+Dog
+Skateboarding
+Fighting game
+Basketball moves
+Wedding
+Skateboard
+IPhone
+Personal computer
+Truck
+Boat
+Railroad car
+Snare drum
+American football
+Drawing
+Pokémon
+Winter sport
+Tractor
+Naruto
+Grand Theft Auto V
+Cymbal
+Horse
+House
+Festival
+Engine
+Highlight film
+Boxing
+World of Warcraft
+Call of Duty: Black Ops II
+Four-wheel drive
+Bird
+Violin
+Skateboarding trick
+Christmas
+Weight training
+Recreational fishing
+Warcraft
+Ice skating
+Driving
+Video game console
+Microsoft Windows
+Airline
+Pokémon (video game series)
+Landing
+Combat
+League of Legends
+Vegetable
+Model aircraft
+Airliner
+Samsung Galaxy
+Sport utility vehicle
+Electronic keyboard
+Hockey
+Radio-controlled aircraft
+???
+Eye shadow
+Cooking show
+Dessert
+Battlefield (series)
+Slam dunk
+Plant
+Painting
+Drifting (motorsport)
+Rallying
+Lego
+Tablet computer
+Call of Duty: Modern Warfare 2
+Comedy (drama)
+Grand Theft Auto: San Andreas
+Off-road vehicle
+The Walt Disney Company
+Locomotive
+Takeoff
+RuneScape
+Puppy
+Amusement park
+Call of Duty: Modern Warfare 3
+Motocross
+Dragon Ball
+Airport
+Photography
+Call of Duty: Black Ops
+Shoe
+Radio-controlled car
+Sonic the Hedgehog
+Skatepark
+Bride
+First-person shooter
+Accordion
+Jet aircraft
+Mascara
+Halo (series)
+Camera
+Final Fantasy
+Skiing
+Gym
+Aviation
+Mountain bike
+Marching band
+???
+Extreme sport
+FIFA 15
+Brass instrument
+Sasuke Uchiha
+Cat
+Sedan (automobile)
+Pickup truck
+Meat
+BMW
+Parade
+Cake
+Supercar
+Aquarium
+Weather
+Weapon
+Nail (anatomy)
+Surfing
+PlayStation 3
+Room
+Call of Duty 4: Modern Warfare
+Helicopter
+Laptop
+Saxophone
+Star Wars
+Goku
+Hotel
+Xbox 360
+Arcade game
+Doll
+News presenter
+Exhaust system
+Volkswagen
+Hatchback
+Action figure
+Computer
+Carnival
+Lipstick
+Wii
+Sonic the Hedgehog (character)
+School
+Ballet
+Eye liner
+Heavy equipment
+IPad
+Running
+Baking
+Rapid transit
+Coupé
+Road bicycle
+Card game
+Nail polish
+Playing card
+Bus
+Counter-Strike (video game)
+Gardening
+Outline of meals
+Nail art
+Tank
+???
+Bollywood
+Tennis
+Ship
+BMX bike
+Drink
+Grand Theft Auto IV
+Snowboarding
+Mountain biking
+Rouge (cosmetics)
+Super Smash Bros.
+???
+Street Fighter
+Stadium
+Underwater
+Hunting
+Kickflip
+Metin2
+The Sims
+Viola
+Pony
+PlayStation 4
+Television
+???
+Beach
+Manicure
+Chocolate
+Wood
+Snow
+Sneakers
+???
+Roller coaster
+Afro-textured hair
+Timbales
+Need for Speed
+Robot
+Paper
+Gymnastics
+Farm
+Diatonic button accordion
+Fighter aircraft
+Sketch (drawing)
+Mercedes-Benz
+Chevrolet
+Batman
+Loudspeaker
+Tool
+Nike, Inc.
+Race track
+Ski
+Underwater diving
+Computer hardware
+Garden
+Paint
+Cello
+Digital camera
+Scooter (motorcycle)
+Motorboat
+Harry Potter
+???
+GoPro
+Assassin's Creed
+Fishing rod
+Battlefield 3
+IPod
+Nature
+Dota 2
+Tree
+My Little Pony
+Dress
+Xbox One
+Train station
+Firefighter
+Jeep
+Rail transport modelling
+Resort
+Flute
+Touhou Project
+Fruit
+Chicken as food
+Knife
+Dashcam
+Clash of Clans
+Kitchen
+Slide show
+The Legend of Zelda
+Fireworks
+Swimming pool
+Rugby football
+Building
+Kitten
+Television advertisement
+???
+Battlefield 4
+Horse racing
+MapleStory
+Subwoofer
+Flour
+IPod Touch
+World of Tanks
+Music festival
+Comedian
+Figurine
+Kingdom Hearts
+Manga
+Wrestling
+Trumpet
+Xbox
+Model (person)
+Jumping
+Dough
+FIFA 13
+Pro Evolution Soccer
+Resident Evil
+Eye
+Guitar Hero
+Enduro
+Home appliance
+News program
+Watch
+Audi
+Off-road racing
+Ice dancing
+Construction
+Organ (music)
+PlayStation Portable
+Figure skating
+Fiddle
+WWE 2K
+Climbing
+Spider-Man
+Braid
+Muscle
+The Elder Scrolls V: Skyrim
+Nintendo 3DS
+Fire
+Human swimming
+BMW Motorrad
+One Piece
+Wildlife
+Apartment
+Dressage
+Scuba diving
+Call of Duty: Ghosts
+Eating
+Kickboxing
+Egg as food
+Origami
+The Elder Scrolls
+Ford Mustang
+Fishing lure
+Light
+Running back
+Air force
+M.U.G.E.N
+Transformers
+Living room
+Soldier
+Bag
+Ballroom dance
+Gohan
+Kayak
+Sheet music
+Destiny (video game)
+Wall
+Church (building)
+Sewing
+Chipmunk
+Surfboard
+Concealer
+Drag racing
+Mega Man
+Walt Disney World
+Chicken
+Parachuting
+Classic car
+Furniture
+Jewellery
+Recreational vehicle
+Call of Duty: Advanced Warfare
+Street Fighter IV
+Sakura Haruno
+Restaurant
+Halo 3
+Wheelie
+Mario Kart
+Headphones
+Factory
+Yu-Gi-Oh! Trading Card Game
+Speedometer
+Circus
+Muscle car
+Bedroom
+Tekken
+Graffiti
+River
+Lighting
+Guitar amplifier
+Knitting
+Call of Duty: Zombies
+PlayStation
+Radio-controlled helicopter
+Cookware and bakeware
+Trail
+Camping
+University
+Indian cuisine
+Multiplayer online battle arena
+Ball
+Nightclub
+Book
+Lego minifigure
+PlayStation 2
+Dodge
+Garry's Mod
+Camera lens
+Hockey puck
+Barbie
+Thomas the Tank Engine
+Go-kart
+Vegetarian cuisine
+Monster High
+Yacht
+Collectible card game
+Auto Race (Japanese sport)
+Role-playing game
+Madden NFL
+Unidentified flying object
+Longboard (skateboard)
+Toddler
+Digital single-lens reflex camera
+Xbox (console)
+Rail freight transport
+Honda Civic
+Convertible
+The Sims 2
+Lamborghini
+Printer (computing)
+Cream
+Parrot
+Tire
+Quadcopter
+Littlest Pet Shop
+Wii U
+Planet
+???
+The Sims 3
+Sony Xperia
+Salad
+Sailboat
+Cruise ship
+Unmanned aerial vehicle
+Naruto: Ultimate Ninja
+Barbecue
+Mortal Kombat
+Slot machine
+Longboarding
+Halo: Reach
+Paragliding
+Bread
+Monster Hunter
+Stitch (textile arts)
+Dofus
+StarCraft II: Wings of Liberty
+Game controller
+Gears of War
+Mud bogging
+Snowboard
+Synthesia
+Wig
+Road bicycle racing
+Wheel
+Macintosh
+Home improvement
+Printing
+Insect
+Road
+Parachute
+Cattle
+Hair coloring
+IPhone 4S
+Advertising
+Potato
+Runway
+Van
+Zoo
+Handheld game console
+Water
+Rock Band
+Volkswagen Golf
+Bathroom
+Stunt performer
+Bleach (manga)
+Metal Gear
+Santa Claus
+Hiking
+Samsung Electronics
+Runway (fashion)
+Elevator
+Cricket
+Gran Turismo (series)
+Fire engine
+Kinder Surprise
+Play-Doh
+Grilling
+Eyelash
+Table tennis
+Fiat Automobiles
+Dragon
+Lion
+Nintendo Entertainment System
+PlayStation (console)
+Stallion
+Ice skate
+Baseball park
+Flamenco
+Steam engine
+Plough
+Farming Simulator
+Soup
+Snowmobile
+Mare
+Counter-Strike: Source
+Sail
+Squat (exercise)
+Bass (fish)
+Banjo
+Harmonica
+Quartet
+Drum stick
+IPhone 5
+Reptile
+Prayer
+T-shirt
+Talent show
+Rice
+Roasting
+Diablo III
+CrossFire (video game)
+Renault
+Pizza
+Trombone
+Chevrolet Camaro
+Barbell
+Ryu (Street Fighter)
+Clay
+Beyblade
+Lake
+Sauce
+???
+Cube
+Forza (series)
+Cookie
+Taiko no Tatsujin
+Mixtape
+Medicine
+Door
+Monster
+Call of Duty: World at War
+Mud
+Computer keyboard
+Clarinet
+Defense of the Ancients
+Sora (Kingdom Hearts)
+Computer monitor
+Super Street Fighter IV
+PlayStation Vita
+Guild Wars
+Album
+Model car
+Tenor saxophone
+The Twilight Saga (film series)
+Rubik's Cube
+Sailor Moon
+Teacher
+Mixing console
+Card manipulation
+Combine harvester
+Boeing 737
+Bull
+Fish as food
+Cheese
+Concrete
+Board game
+Moped
+Puzzle
+Lego Star Wars
+Poker
+Portrait
+Luigi
+Dining room
+Pokémon X and Y
+Floor
+Asus
+Inuyasha
+Livestock
+Lawn mower
+Tibia (video game)
+Tabletop game
+Iron Man
+Tomato
+Juice
+Final Fantasy VII
+Lip gloss
+Super Smash Bros. Melee
+Central processing unit
+Sitcom
+Cockpit
+Emergency vehicle
+FIFA 12
+Bodyboarding
+Earth
+The Lego Group
+Ice cream
+Microphone
+Rallycross
+Website
+Table (furniture)
+Ice
+Magic: The Gathering
+Ninja
+Darth Vader
+Saw
+Mickey Mouse
+Handbag
+The King of Fighters
+Ballet dancer
+Samsung Galaxy Note series
+Washing machine
+Zee TV
+Point Blank (2008 video game)
+Gibson Les Paul
+Dune buggy
+DayZ (video game)
+Television set
+Dirt track racing
+Edward Cullen
+Beauty salon
+Hetalia: Axis Powers
+Vampire
+Gliding
+Batman: Arkham
+Mountain
+Rain
+Shark
+Waterfall
+DarkOrbit
+Bagpipes
+Comics
+Rock climbing
+Skin
+Arena
+IPhone 4
+ARMA (series)
+Super Smash Bros. for Nintendo 3DS and Wii U
+Curry
+Pasta
+Halo 4
+Superman
+Icing (food)
+Google Nexus
+Marathon
+Deer
+Guitar Hero III: Legends of Rock
+Balloon
+Goalkeeper (association football)
+Red Bull
+Nissan GT-R
+Noodle
+Fishing bait
+Pencil
+Plants vs. Zombies
+Athlete
+Computer case
+Stretching
+Terrier
+Outer space
+Textile
+Mercedes-AMG
+Hard disk drive
+Biceps
+Handball
+Land Rover
+Kamen Rider Series
+Parakeet
+Bear
+Rim (wheel)
+Chevrolet Corvette
+Battery (electricity)
+Milk
+Roblox
+BMW M3
+Christmas decoration
+Moon
+Microsoft Lumia
+Combat Arms (video game)
+Maize
+Cargo
+Headset (audio)
+Bee
+Helmet
+Street art
+Clown
+Tattoo
+Cupcake
+Traxxas
+Money
+Hatsune Miku: Project DIVA
+Bead
+Angry Birds
+Movieclips
+Optimus Prime
+MacBook
+Mass Effect
+Bowser (character)
+Sega Genesis
+Pachinko
+Jedi
+Jeep Wrangler
+Dragon Ball Z: Budokai Tenkaichi
+Tales (series)
+Loader (equipment)
+Water park
+Beef
+Sewing machine
+Beer
+Glass
+Silage
+Seafood
+Gran Turismo 5
+Harp
+Joker (comics)
+Volkswagen Beetle
+???
+BlackBerry
+AdventureQuest Worlds
+Bowling
+Guild Wars 2
+Dragon Quest
+Washing
+Mermaid
+Cue stick
+Boot
+Stir frying
+Grand Theft Auto: Vice City
+Penguin
+Acrylic paint
+Cocktail
+Kingdom Hearts II
+Coral
+Borderlands 2
+Telephone
+Gears of War (video game)
+Far Cry
+Tractor pulling
+Rock Band (video game)
+Crane (machine)
+Updo
+Stuffed toy
+Lawn
+Tekken (video game)
+Airbus A320 family
+IPhone 5S
+Watercolor painting
+Ten-pin bowling
+Duck
+Pokémon Trading Card Game
+Oven
+Subaru Impreza
+Porsche 911
+Backpack
+Carl Johnson (Grand Theft Auto)
+German Shepherd
+Turtle
+Metal
+Left 4 Dead
+Ultralight aviation
+Comic book
+Batting (cricket)
+Tram
+Mower
+Reef aquarium
+???
+Swing (dance)
+Lego City
+Game Boy Advance
+Diesel engine
+Pitcher
+Dance studio
+Hamburger
+Cake decorating
+Left 4 Dead 2
+Bible
+Candy
+Vacuum cleaner
+Pokémon Omega Ruby and Alpha Sapphire
+Sowing
+Roof
+Donkey Kong
+Trout
+Coin
+Tent
+Digimon
+Costume
+Warface
+Sandwich
+BMW 3 Series
+Star Wars: The Old Republic
+Trampoline
+Pipe organ
+Latin dance
+Aerobics
+Aion: Upheaval
+Supermoto
+Netbook
+Gift
+Strum
+Mitsubishi Lancer Evolution
+Drum and bugle corps (modern)
+Gramophone record
+Gundam (mobile suit)
+Euro Truck Simulator 2
+Tai chi
+Teenage Mutant Ninja Turtles
+Aerobatics
+Wedding dress
+Hair conditioner
+Achievement (video gaming)
+Boeing 777
+Shadow the Hedgehog
+Boeing 747
+Simba
+Silkroad Online
+Kindergarten
+Smartwatch
+Computer mouse
+Bell
+Museum
+Rabbit
+Total War (series)
+DVD
+Devil May Cry
+Face
+Lathe
+Five Nights at Freddy's
+Logging
+String quartet
+Bridge
+Super Mario Bros.
+Fishing reel
+Badminton
+Clock
+Stove
+Wine
+Subaru
+Leather
+IPad 2
+Terraria
+Attack on Titan
+Bottle
+Kick
+Police officer
+Raw foodism
+Video card
+Alpine skiing
+String (music)
+StarCraft (video game)
+Roadster (automobile)
+Steak
+Hearthstone (video game)
+Solo dance
+Foreign exchange market
+God of War (series)
+Hulk (comics)
+Easter egg
+Ceiling
+Yo-kai Watch
+Wakeboarding
+Monster truck
+McDonald's
+Assassin's Creed III
+Chopper (motorcycle)
+Largemouth bass
+Roller skating
+Glider (aircraft)
+Jacket
+Marimba
+Christmas tree
+Sand
+Afro
+MacBook Pro
+Booster pack
+Dark Souls II
+Bartender
+Quarterback
+Illustration
+ARMA 2
+Star Trek
+Itachi Uchiha
+Hot rod
+Saints Row
+Freeza
+Need for Speed: Most Wanted (2012 video game)
+Hair twists
+Super Mario World
+Crash Bandicoot
+Pork
+Shampoo
+Mask
+Hair iron
+Marvel vs. Capcom
+Castlevania
+Halo 2
+Battery charger
+Tower defense
+BBC
+Kawasaki motorcycles
+Link (The Legend of Zelda)
+Muffler
+Nintendo 64
+Marriage proposal
+Fingerboard (skateboard)
+Beehive
+Pokémon HeartGold and SoulSilver
+Bowling ball
+Tower of Saviors
+Artificial nails
+Final Fantasy XIII
+Chair
+Hijab
+Juggling
+Nissan Skyline
+Anpanman
+Car wash
+Kite
+Diablo (video game)
+Resident Evil 4
+Candy Crush Saga
+Rocket
+Video game arcade cabinet
+Whale
+Glider (sailplane)
+Flooring
+Kingdom Hearts (video game)
+???
+Fast food
+Mandolin
+Metal detector
+Cinema 4D
+Ash Ketchum
+Router (computing)
+Yamaha YZF-R1
+Uncharted
+DC Comics
+Egg
+Lexus
+Ollie (skateboarding)
+Hamster
+Chainsaw
+Galaxy
+Embroidery
+Suite (hotel)
+Brush
+Electronic drum
+Gran Turismo 6
+NBA 2K15
+Dolphin
+Salmon
+Window
+Drill
+Pen
+Backpacking (wilderness)
+Torte
+Web page
+Dreadlocks
+Hot Wheels
+Brake
+Tuba
+Volcano
+Ibiza
+Dragon Age
+Mini
+Perfect World (video game)
+Knot
+Tails (character)
+Thunderstorm
+Video camera
+Smoothie
+Crossover (automobile)
+Condominium
+Desert
+Pump
+Strawberry
+Coffeemaker
+The Legend of Zelda: Ocarina of Time
+Tarot
+Architecture
+Portal (video game)
+Dynasty Warriors
+Lightning McQueen
+Pirates of the Caribbean (film series)
+Tile
+Battlefield: Bad Company 2
+Sketch comedy
+Aikido
+V8 engine
+Sailor Moon (character)
+Lamborghini Aventador
+Carp fishing
+Kirby (series)
+Banana
+Police car
+Laser lighting display
+Necklace
+???
+WWE '13
+Mini (marque)
+Tanki Online
+Oil
+Radio-controlled boat
+Dinosaur
+Pie
+President of the United States
+NBA 2K14
+Labrador Retriever
+Blender
+Plarail
+Captain America
+Electric locomotive
+Street racing
+Need for Speed: Most Wanted (2005 video game)
+Canoe
+Golf club
+Sheep
+Bar
+CDJ
+Lace
+Gold
+Glove
+Halo: Combat Evolved
+Alphabet
+Fender Telecaster
+IPhone 3GS
+Beadwork
+Personal water craft
+Dietary supplement
+James Bond
+Ragnarok Online
+French braid
+Road racing
+Star
+Dean Winchester
+Snake
+Seed
+Christmas lights
+Plaster
+Trunks (Dragon Ball)
+Forage harvester
+Cartoon Network
+Honda CBR series
+Battlefield Hardline
+Tekken 6
+Glitter
+Ford Focus
+Roland V-Drums
+Ski-Doo
+Tyrannosaurus
+New Super Mario Bros.
+Cue sports
+Rainbow Loom
+Samsung Galaxy S III
+Glasses
+Italian cuisine
+RollerCoaster Tycoon 3
+Pig
+Lock (security device)
+The Lord of the Rings (film series)
+Military parade
+Elephant
+Pull-up (exercise)
+Eyelash extensions
+Ring (jewellery)
+Minivan
+Coca-Cola
+Mural
+Love song
+Portal 2
+Mortal Kombat (2011 video game)
+Yarn
+Pokémon Ruby and Sapphire
+Dragon Nest
+Japanese cuisine
+Resident Evil 5
+Jeans
+Map
+Pikachu
+Sun
+Pond
+Bulldog
+Greenhouse
+Škoda Auto
+Baby transport
+Apple
+The Doctor (Doctor Who)
+Turbine
+Naruto: Ultimate Ninja Storm
+Watch Dogs
+VHS
+Ariel (Disney)
+Sculpture
+Bulldozer
+Transformice
+Sushi
+Home run
+Fountain
+Slopestyle
+Fullmetal Alchemist
+Ultimate Marvel vs. Capcom 3
+Automotive lighting
+Lightsaber
+Chevrolet Silverado
+Honey
+Wangan Midnight
+Sword
+Toilet
+Super Mario Galaxy
+Akuma (Street Fighter)
+Shiva
+Bed
+Toy train
+Manufacturing
+Ram Trucks
+Stuffing
+Biscuit
+Kia Motors
+Spa
+Samsung Galaxy S II
+Demolition
+Airbus A330
+Breakfast
+Airbus A380
+Pancake
+Kawasaki Ninja
+Mitsubishi Lancer
+Mushroom
+Grand Theft Auto: The Lost and Damned
+Microsoft Flight Simulator
+Spacecraft
+Logo
+Stock car racing
+Goat
+Pool (cue sports)
+Assassin's Creed (video game)
+Majin Boo
+Vespa
+???
+Samsung Galaxy S4
+Assassin's Creed IV: Black Flag
+Batman: Arkham City
+Monkey
+Death Note
+WWE 2K15
+Pumpkin
+Shopping mall
+Rose
+Cola
+Minnie Mouse
+Caporales
+Jet Ski
+World of Warcraft: Wrath of the Lich King
+Winter
+Prom
+Karaoke box
+Minibike
+RFactor
+Art exhibition
+Plush
+Chocolate cake
+Ford F-Series
+Soap
+Knuckles the Echidna
+Dump truck
+Giant panda
+Dance Dance Revolution
+Princess
+Street food
+Flashlight
+Animal Crossing
+Pilates
+Pipe band
+Toyota Land Cruiser
+Lara Croft
+Jumbotron
+Ferrari F430
+Cell (Dragon Ball)
+BMW 3 Series (E36)
+Injustice: Gods Among Us
+Dumbbell
+Samsung Galaxy Tab series
+Bodyweight exercise
+Penalty kick (association football)
+Lizard
+City
+Bionicle
+Kirby (character)
+WWE 2K14
+Pokémon Battle Revolution
+Sonic the Hedgehog (1991 video game)
+Alliance of Valiant Arms
+Racket (sports equipment)
+K-1
+Acer Inc.
+Recorder (musical instrument)
+Earring
+National park
+The Elder Scrolls IV: Oblivion
+Audi R8
+Clothes dryer
+Military band
+Silver
+Warcraft III: Reign of Chaos
+Classroom
+Samsung Galaxy S5
+Black cat
+Scarf
+Kratos (God of War)
+Skylanders
+Super Robot Wars
+Electric car
+Video lesson
+Smoking (cooking)
+Antenna (radio)
+Sonic Generations
+Butter
+Chess
+Hello Kitty
+Goldfish
+Carrot
+Blu-ray
+Squirrel
+Balloon (aeronautics)
+Microwave oven
+Range Rover
+Wool
+TalesRunner
+IPad Mini
+Pokémon Emerald
+Inflatable boat
+Bull riding
+Football boot
+Gears of War 2
+Bugatti Veyron
+Airbrush
+Brick
+Avengers (comics)
+Plants vs. Zombies 2: It's About Time
+United States Navy
+Ball (association football)
+Volkswagen Gol
+Yo-yo
+Forza Motorsport 4
+Logitech
+Shirt
+Golden Retriever
+Alarm device
+Water slide
+Paramotor
+Fondant icing
+Acrobatic gymnastics
+Coach (sport)
+The Witcher 3: Wild Hunt
+Tabla
+Kinect
+Zee Bangla
+???
+Cabinetry
+Quilt
+Claw crane
+Spyro (series)
+Yoshi
+Tekken Tag Tournament 2
+Diamond
+Samsung Galaxy S series
+BMW 3 Series (E46)
+Tiger
+Number
+Traffic
+Metalworking
+Haruhi Suzumiya
+Gown
+Luxury yacht
+Yuna (Final Fantasy)
+Station wagon
+Softball
+The Legend of Zelda: Twilight Princess HD
+Dungeon Fighter Online
+Plasticine
+LG Optimus series
+Source (game engine)
+Battlefield 2
+BMW 3 Series (E30)
+Ink
+Half-Life 2
+Hitman (series)
+Inline skates
+Remote control
+Mercedes-Benz C-Class
+The Sims 4
+Harlem Shake (meme)
+Magic Kingdom
+Dune
+Prince of Persia
+Final Fantasy XIV
+Marvel Universe
+Draco Malfoy
+Ram Pickup
+DC Universe Online
+Assassin's Creed II
+Mars
+Xylophone
+Dragon Age: Inquisition
+Game Boy
+Carpet
+Roxas (Kingdom Hearts)
+Balance beam
+Mass Effect 2
+Dragon Ball Xenoverse
+Call of Duty: Black Ops – Zombies
+Cadillac
+Guinea pig
+The Hobbit (film series)
+Need for Speed: World
+Pastry
+Chapel
+Rayman
+Armour
+Mouse
+Assassin's Creed: Brotherhood
+Lord Voldemort
+Magnet
+The Sims (video game)
+Rubber band
+Grocery store
+Reborn doll
+Ford GT
+WWE '12
+PlanetSide 2
+Jaguar Cars
+Volvo Cars
+Jeep Cherokee (SJ)
+Homer Simpson
+USB flash drive
+Torero
+Persona (series)
+Model railroad layout
+Buttercream
+Serve (tennis)
+Ferrari 458
+Honda Accord
+Chevrolet Impala
+Command & Conquer
+Warframe
+Chrysler (brand)
+Standup paddleboarding
+Pretty Cure
+Campsite
+Final Fantasy VIII
+Audi A4
+Sailing ship
+Rafting
+Custom car
+Belle (Disney)
+Rowing (sport)
+Jeep Grand Cherokee
+Wire
+BMW M5
+Hula hoop
+Pinball
+Spaghetti
+Monster Hunter Freedom Unite
+Far Cry 4
+Pro Evolution Soccer 2015
+Test Drive (series)
+Motorcycle helmet
+Router (woodworking)
+Cave
+Cheesecake
+Birthday cake
+Suzuki Jimny
+New Super Mario Bros. Wii
+Ezio Auditore da Firenze
+Fisherman
+Mime artist
+Roller skates
+Pump It Up (video game series)
+Dissidia Final Fantasy
+Supercharger
+Gemstone
+Titanfall
+Downhill
+Medal
+Garbage truck
+Forehand
+Heroes of Newerth
+Plastic
+???
+Astronaut
+Guitar Hero World Tour
+ArcheAge
+Lowrider
+Police dog
+Toyota Corolla
+Ford Fiesta
+Helmet camera
+Cabal Online
+Assassin's Creed Unity
+Ceramic
+Kidō Senshi Gundam: Senjō no Kizuna
+Hot air balloon
+Shower
+Donald Duck
+Multi Theft Auto
+Rock Band 3
+Porsche 911 GT3
+Stick figure
+Sled
+Lemon
+Frog
+Mexican Creole hairless pig
+Forklift
+Dog agility
+Kettlebell
+Shelby Mustang
+Candle
+Bowling (cricket)
+Kick (football)
+Electric vehicle
+Oboe
+Desktop computer
+Wing Chun
+Statue
+DayZ (mod)
+Eagle
+Fire station
+Nike Air Max
+Rage (video game)
+Woodturning
+Fireplace
+Volkswagen Jetta
+Madison Square Garden
+Fly tying
+Spore (2008 video game)
+Hammond organ
+Sam Winchester
+The Pink Panther
+Saints Row: The Third
+Cherry blossom
+Doraemon
+WWE action figures
+Marvel vs. Capcom 3: Fate of Two Worlds
+Bugatti Automobiles
+Fire Emblem
+Border Collie
+Aircraft carrier
+Snow blower
+Culinary art
+Ken Masters
+Seafight
+Sport bike
+Dentist
+Easter egg (media)
+Joystick
+Tuna
+Crysis 2
+Audi Quattro
+Academy Awards
+Ponytail
+Ramen
+Hummer
+Fishing tackle
+Final Fantasy X-2
+Coupon
+Porsche Carrera
+Wood carving
+Rocksmith
+Wallet
+Refrigerator
+Koi
+Battlefield Heroes
+Phonograph
+Onion
+Biceps curl
+Trainz
+Hat
+Jubeat
+Nissan Skyline GT-R
+Mattel
+GameCube
+LittleBigPlanet 2
+Epiphone
+Inazuma Eleven
+Soft tennis
+Killer whale
+Hair straightening
+Merienda
+The Witcher (video game)
+Skate (video game)
+Live for Speed
+Rooster
+Chihuahua (dog)
+Triangle
+Land Rover Defender
+Marvel Legends
+Trousers
+SD Gundam Capsule Fighter
+Ratchet & Clank
+Doughnut
+Hatsune Miku: Project DIVA F
+Bouzouki
+Domestic canary
+Half-Life (video game)
+Raven (comics)
+Black Butler
+Mario Kart 8
+Chili pepper
+BMW 5 Series
+Hail
+Ouran High School Host Club
+Brain
+Chinese cuisine
+Playmobil
+Model building
+Ribbon
+Pit bike
+Sonic Unleashed
+Solar panel
+Orange (fruit)
+Otis Elevator Company
+Mu Online
+Hang gliding
+Path of Exile
+Animal Crossing: New Leaf
+Steel guitar
+Sword Art Online
+Lego Ninjago
+Paddle
+Second Life
+Aikatsu!
+IPhone 5C
+Gothic (series)
+Batman: Arkham Asylum
+Carburetor
+Crab
+Espresso machine
+The Phantom of the Opera (1986 musical)
+Hellsing
+Spider
+Super Mario Galaxy 2
+Duel Masters Trading Card Game
+Drywall
+Laundry
+United States Air Force
+Assassin's Creed: Revelations
+Corel
+Omelette
+Composer
+Ford Escort (Europe)
+Grape
+Honda CB600F
+Tea
+Elmo
+Temple
+Need for Speed: Carbon
+Catamaran
+Perfect World (company)
+Skate 3
+Missile
+Infomercial
+Chevrolet Chevelle
+Airport terminal
+Crysis (video game)
+StepMania
+Red Dead Redemption
+Atari
+Couch
+The Idolmaster
+Beatmania IIDX
+Big wave surfing
+Tokyo Mew Mew
+Wheat
+Warhammer Fantasy Battle
+Rock (geology)
+Snowplow
+Submarine
+Doctor Eggman
+Wood flooring
+Bangs (hair)
+Yamaha YZF-R6
+Pontiac Firebird
+Red Dead
+Field hockey
+Vineyard
+Waterfowl hunting
+Domestic pigeon
+Toyota Hilux
+CNET
+Preacher
+Sonic Adventure
+Lamborghini Murciélago
+Marinera
+Screen printing
+Crazyracing Kartrider
+The Legend of Zelda: Majora's Mask
+Sunglasses
+Log cabin
+Fungus
+Wedding photography
+Flag
+Devil May Cry 4
+Cappuccino
+Flamenco guitar
+Projector
+Rock dove
+The Elder Scrolls Online
+LittleBigPlanet (2008 video game)
+Digital video recorder
+Djembe
+Vending machine
+Mehndi
+Telescope
+Flyff
+Pattern (sewing)
+Stairs
+Nissan 350Z
+Cell (biology)
+Need for Speed: Underground 2
+Incandescent light bulb
+Gallon
+Greeting card
+Balloon modelling
+Sensor
+Realm of the Mad God
+Nest
+Writing
+Logic Pro
+Opel Astra
+Campervan
+Cooked rice
+Muffin
+Wind power
+Hedgehog
+Soft drink
+Calculator
+Harness racing
+Buick
+Beast (Disney)
+Destroyer
+Point guard
+Forza Horizon
+Mercedes-Benz SLS AMG
+Supermarket
+Catfish
+Final Fantasy XI
+The Last of Us
+Battleship
+Dodge Challenger
+Peter Pan
+Metal Gear Solid 4: Guns of the Patriots
+Toyota 86
+Bakery
+Compact disc
+Backhoe
+Saddle
+Total Drama Island
+Erhu
+Bumblebee (Transformers)
+Cajón
+Beatmania
+Ice rink
+Child safety seat
+Honda S2000
+Samsung Galaxy Note II
+Higurashi When They Cry
+Union Pacific Railroad
+BMW 3 Series (E90)
+V6 engine
+BlazBlue
+Rottweiler
+Necktie
+Image scanner
+White-tailed deer
+TV4 (Sweden)
+Bishop
+Need for Speed: Hot Pursuit (2010 video game)
+Princess Peach
+Rust (video game)
+Doom (1993 video game)
+Fender Custom Shop
+Smite (video game)
+Nissan Silvia
+???
+Pudding
+Sephiroth (Final Fantasy)
+Irish dance
+MacBook Air
+Commodore 64
+IMac
+Space Shuttle
+Automobile repair shop
+Collie
+Dragon Age: Origins
+Sangokushi Taisen
+Calligraphy
+Black belt (martial arts)
+???
+Valve
+Crisis Core: Final Fantasy VII
+Two-stroke engine
+Killzone (series)
+Full moon
+Hunter × Hunter
+New York City Subway
+Latte
+Mercedes-Benz S-Class
+Tetris
+Samurai
+Predator (alien)
+Arabian horse
+Mercedes-Benz E-Class
+Spinach
+Dōjinshi
+Polar bear
+Body piercing
+Amazon Kindle
+Biology
+Key (lock)
+Mobile Suit Gundam: Extreme Vs.
+Rappelz
+Bobber (motorcycle)
+Toy balloon
+Mexican cuisine
+Rope
+Taco
+Taxicab
+Infestation: Survivor Stories
+Clutch
+PlayStation Network
+Garage (residential)
+Milkshake
+Cloud Strife
+Honda Integra
+Eintopf
+Primary school
+Kingdom Hearts Birth by Sleep
+Resident Evil (1996 video game)
+Foal
+GameSpot
+Castle
+Human hair color
+Scorpion (Mortal Kombat)
+Poultry
+Poodle
+Vans
+Forza Horizon 2
+Zero (Mega Man)
+Toyota Camry
+Chemical reaction
+Test Drive Unlimited 2
+Bacon
+Mario Party
+18 Wheels of Steel
+Goose
+Sausage
+Compost
+Cucumber
+French horn
+Analog synthesizer
+Siamese fighting fish
+???
+Las Vegas Strip
+Crysis 3
+School bus
+Oculus Rift
+Carnival Cruise Line
+Honda CBR600RR
+Pokémon Red and Blue
+Autobot
+Christ (title)
+Cockatiel
+Ace Combat
+Mazda MX-5
+Countertop
+Safari
+Final Fantasy XIV: A Realm Reborn
+Track (rail transport)
+Ganon
+Two-wheel tractor
+???
+Watermelon
+Paper plane
+Rainbow trout
+???
+Tony Hawk's (series)
+Korean cuisine
+Lip balm
+Angry Birds (video game)
+Lead guitar
+Pug
+Monster Hunter Tri
+Playground
+God of War III
+Herd
+Niko Bellic
+Bungee jumping
+Soil
+Subway Surfers
+Hindu temple
+Audi A6
+Hogwarts
+Eggplant
+Mabinogi (video game)
+Sugar
+Makeup brush
+Rocksmith 2014
+Ocean
+Asphalt (series)
+Dental braces
+Bob cut
+Nissan 240SX
+Cement
+Sharpening
+Leopard
+United States Army
+Tom and Jerry
+Xbox 360 controller
+Dragon Ball: Raging Blast 2
+Winnie the Pooh (franchise)
+Trophy
+Inazuma Eleven (manga)
+Owl
+Street Fighter II: The World Warrior
+Golf ball
+Floyd Mayweather Jr. vs. Manny Pacquiao
+Belt (clothing)
+Slender: The Eight Pages
+Test Drive Unlimited
+Super Mario Bros. 3
+Power supply
+Retail
+Venom (comics)
+IPad (3rd generation)
+Teddy bear
+Denim
+Baseball bat
+Halo 3: ODST
+Train Simulator (Dovetail Games)
+Bowhunting
+Lotus Cars
+Pineapple
+Boeing 737 Next Generation
+Audi A3
+Dreamcast
+City-building game
+Diablo II
+Suzuki Hayabusa
+Gamepad
+Electrical wiring
+Kitchen stove
+Yamaha Aerox
+Monster Hunter Portable 3rd
+BMX racing
+Katara (Avatar: The Last Airbender)
+HP Pavilion (computer)
+Emirates (airline)
+Amiga
+Touchscreen
+Winter storm
+Driver (video game series)
+Pac-Man
+Fantage
+Land Rover Discovery
+Flash (photography)
+Human back
+Intermodal container
+Infiniti
+Guilty Gear
+Animal shelter
+Butterfly
+Piccolo (Dragon Ball)
+Bicycle frame
+Boeing 787 Dreamliner
+Toontown Online
+Renault Mégane
+Age of Empires
+Canyon
+Ski jumping
+Lumber
+Carousel
+Phantasy Star Online 2
+Dodge Viper
+Madden NFL 13
+A-18 Hornet
+String trimmer
+Mattress
+Mixer (cooking)
+Sub-Zero (Mortal Kombat)
+Ford Ranger (North America)
+ESPN
+ABS-CBN News and Current Affairs
+Synchronised swimming
+G-Shock
+???
+Angel
+Champion
+Horse show
+???
+Rurouni Kenshin
+Halo 5: Guardians
+Coconut
+Deep frying
+Dollhouse
+Campus
+Volkswagen Golf Mk6
+Curtain
+Mountain pass
+Dojo
+Boiler
+PRS Guitars
+Diesel locomotive
+Monster Hunter 4
+French Bulldog
+Prince (Prince of Persia)
+Fixed-gear bicycle
+Ninja Gaiden
+Samsung Galaxy Note 3
+Opel Corsa
+Jack Sparrow
+Boeing 767
+Lexus IS
+Tales of Symphonia
+Autumn
+Inline skating
+Filter (aquarium)
+Naruto Shippuden: Ultimate Ninja Storm Generations
+Garmon
+Flower bouquet
+SimCity
+Gravy
+Bully (video game)
+French fries
+Kawasaki Ninja 250R
+Rock fishing
+Batman: Arkham Origins
+Ceiling fan
+Audi TT
+Space Marines (Warhammer 40,000)
+Acer Aspire
+D.Gray-man
+Duct tape
+Electromagnetic coil
+Heroes of the Storm
+Tom Clancy's Ghost Recon
+Sponge cake
+Steelpan
+Modem
+The King of Fighters 2002
+Dying Light
+Need for Speed: Shift
+Riot Games
+Rainbow
+Bean
+Chevrolet Opala
+Reborn!
+Floral design
+Megatron
+Kawasaki Ninja ZX-6R
+Agriculture
+Cottage
+Television presenter
+Metal Gear Solid V: The Phantom Pain
+Juicing
+BioShock
+Plymouth (automobile)
+Crêpe
+Fist of the North Star
+The Legend of Zelda: The Wind Waker
+X-Men
+Piston
+Deck (building)
+Nativity scene
+Sega Saturn
+Stardoll
+Just Dance (video game)
+Chun-Li
+BMW R1200GS
+LG G3
+Fisheye lens
+Dragon Ball: Raging Blast
+Big Boss (Metal Gear)
+Dam
+Gel
+JBL
+Dachshund
+Bane (comics)
+E-reader
+The Lord of the Rings Online
+Ferb Fletcher
+Yeast
+Monastery
+Vampire Knight
+Vodka
+IPhone 3G
+Tricycle
+Metal Slug (series)
+Steel
+LED lamp
+Geometry Dash
+Dominoes
+Gibson Les Paul Custom
+Street Fighter III: 3rd Strike
+Hay
+Honda CR-X
+Spray painting
+Flip Video
+Bald eagle
+God of War II
+Clay animation
+Tomato sauce
+Clone trooper
+Beagle
+Popcorn
+Rubber stamp
+Clannad (visual novel)
+Fried rice
+Moto G (1st generation)
+Toyota Prius
+Mega Man Battle Network
+Doom II: Hell on Earth
+Grand Theft Auto: Vice City Stories
+Deadpool
+Phantasy Star
+Lock picking
+Sugar paste
+Chevrolet Caprice
+???
+Herb
+The Legend of Zelda: Skyward Sword
+Domesticated turkey
+Final Fantasy VI
+BMW S1000RR
+Mitsubishi Pajero
+Mazda3
+IKEA
+Chevrolet S-10
+Paper Mario
+India TV
+Tow truck
+Orochimaru (Naruto)
+Ape
+Line (geometry)
+Kawasaki Ninja ZX-10R
+Aerosol spray
+Power supply unit (computer)
+Zucchini
+Doberman Pinscher
+Wolfenstein (series)
+Contortion
+Fertilizer
+Cooler Master
+Highway
+Chocolate brownie
+Street Fighter III
+Tsubasa: Reservoir Chronicle
+Parking
+Olaf (Disney)
+Frets on Fire
+Multi-function printer
+Suzuki GSX-R1000
+Lush (company)
+Hang (instrument)
+Nexus 7 (2012)
+Skyscraper
+Gorilla
+Ōendan
+Puff pastry
+Crossbow
+Forza Motorsport 5
+Uncharted 2: Among Thieves
+Pokémon Mystery Dungeon
+Closet
+???
+Daytona International Speedway
+VTEC
+Cheerleading
+Slot car
+Garden railway
+Albert Wesker
+Naruto Shippuden: Ultimate Ninja Storm 2
+Sewing needle
+Trials (series)
+Sheriff Woody
+K
+Straw
+Mitsubishi Eclipse
+Frisbee
+TrackMania
+Manure
+Chocolate chip
+Cart
+Borderlands: The Pre-Sequel
+Diving
+Wood-burning stove
+Medal game
+Chrono Trigger
+Sherlock Holmes
+Library
+Volkswagen Golf Mk2
+Guzheng
+Malinois dog
+Goofy
+Pedal steel guitar
+Virtua Fighter 5
+Lego Marvel Super Heroes
+Kantai Collection
+Electric violin
+Firewood
+Devil May Cry 3: Dante's Awakening
+Digital painting
+Flair bartending
+Boxer (dog)
+Melon
+Low-carbohydrate diet
+Škoda Octavia
+The Crew (video game)
+Unicycle
+GAZ
+Gummy bear
+Marker pen
+Need for Speed: The Run
+Dead Space (2008 video game)
+Duke Nukem
+Dirt 3
+Movie theater
+Final Fantasy XIII-2
+Comet
+WWE SmackDown vs. Raw 2010
+Gran Turismo 4
+Star Wars: Battlefront II
+Lamb and mutton
+Ant
+Loki (comics)
+Percy the Small Engine
+Villain
+Plumbing
+Avocado
+BioShock Infinite
+Dormitory
+Mango
+Lucky Star (manga)
+Shadow the Hedgehog (video game)
+Cabbage
+Peanut butter
+Didgeridoo
+Hard Rock Cafe
+Donkey Kong Country
+Amazon.com
+Star Wars Battlefront (2015 video game)
+Harpsichord
+Aston Martin Vantage (2005)
+Suzuki Swift
+Crocodile
+Jet engine
+Sonic the Hedgehog 2
+Delta Air Lines
+Harry Potter and the Deathly Hallows
+Trunk (car)
+Zangief
+Brave Frontier
+Chuck E. Cheese's
+Iori Yagami
+Robotics
+Kebab
+Cheeseburger
+Hatsune Miku: Project DIVA F 2nd
+Humbucker
+Camcorder
+Mega Man X (video game)
+Landscape
+Shih Tzu
+Volkswagen Golf Mk4
+Pollution
+Guppy
+Coffeehouse
+Killer Instinct
+Crusher
+Allods Online
+???
+Boeing 757
+Eclipse
+Meatball
+Saints Row 2
+Roulette
+Grand Theft Auto: Liberty City Stories
+Walleye
+Walmart
+Bearing (mechanical)
+Forest
+Forever 21
+Canvas
+Rat rod
+Soulcalibur V
+Sonic the Hedgehog (2006 video game)
+Multirotor
+???
+LG G2
+Moisturizer
+Halo: The Master Chief Collection
+SEAT León
+Skylanders: Swap Force
+Pan flute
+Chevrolet Tahoe
+Metal Gear Online
+Fiat 126
+Mount & Blade: Warband
+Kennel
+Vibraphone
+Satellite
+Yamaha Raptor 700R
+Sonic & Knuckles
+Honda Fit
+Caridea
+Armored Core
+Bull Terrier
+Firefighting
+Catwoman
+Octopus
+Fencing
+Sitar
+Limousine
+Nintendo DSi
+HTC One (M8)
+McDonnell Douglas F-15 Eagle
+Rat
+GoldenEye 007 (1997 video game)
+Gasoline
+Ken (doll)
+Quadracycle
+Dead or Alive (series)
+Microsoft Surface
+Scooby-Doo
+Landscape painting
+Toyota Land Cruiser Prado
+Hair removal
+Sink
+Mount & Blade
+BMW 5 Series (E39)
+Mewtwo
+Mambo (music)
+The Witcher 2: Assassins of Kings
+North American P-51 Mustang
+Alien (creature in Alien franchise)
+Cloud
+Forge
+Christian Church
+Tom Clancy's Rainbow Six
+Mirror
+Chevrolet Big-Block engine
+Chevrolet Corvette (C6)
+Abarth
+Mazda RX-8
+Pendant
+Metal Gear Solid 3: Snake Eater
+Buffet
+Haunted house
+Cockatoo
+Royal Air Force
+The Embodiment of Scarlet Devil
+LG G series
+Fishing vessel
+DualShock
+Sonic Heroes
+Drawer (furniture)
+BMW 1 Series
+Werewolf
+DatPiff
+Koi pond
+Toyota Celica
+Twelve-string guitar
+Potato chip
+Stargate
+Killer Instinct (2013 video game)
+Caramel
+Sprite (computer graphics)
+NHL 14
+Ham
+Sky
+Sweater
+Chocolate chip cookie
+stay night
+Text (literary theory)
+Skate 2
+Engraving
+Final Fantasy XV
+Cornrows
+Light Yagami
+Floristry
+Sly Cooper
+Volkswagen Golf Mk5
+Snowman
+???
+Vox (musical equipment)
+Happy Farm
+Orc
+Suit (clothing)
+PC game
+Ace Online
+Saints Row IV
+Slingshot
+Dead Island
+Ratchet (Ratchet & Clank)
+Gears of War: Judgment
+Dragon Quest X
+Furby
+Crayon Shin-chan
+Soprano saxophone
+Tifa Lockhart
+European perch
+Patio
+Fried chicken
+Sawmill
+Mirror's Edge
+Canon PowerShot
+Guitar Hero: Warriors of Rock
+Rome: Total War
+Hummer H2
+Radar
+Final Fantasy IV
+Table saw
+Barista
+BMW 7 Series
+Camel
+Windows Media Video
+Felt
+Audi S4
+Cowboy
+Molding (process)
+Contact lens
+Fiat Punto
+The Hobbit
+Indoor cycling
+Sunset
+???
+Persian cat
+Hitman: Absolution
+Battlefield: Bad Company
+Eren Yeager
+Sinterklaas
+Crash Bandicoot (video game)
+Midnight Club: Los Angeles
+Metal Gear Rising: Revengeance
+Hand-to-hand combat
+Avon Products
+Log splitter
+Stormtrooper (Star Wars)
+Epic Rap Battles of History
+Shed
+Walking
+Belt (mechanical)
+Hot dog
+Sock
+Chicken coop
+Humpback whale
+Character (arts)
+Peugeot 106
+Toast
+Princess Jasmine
+Exercise ball
+Fox
+Green Lantern
+Looney Tunes
+Wedding ring
+Tap (valve)
+Charizard
+Mii
+Rolls-Royce Limited
+Copic
+Mega Man Zero (video game)
+Jak and Daxter
+Priston Tale
+Glacier
+IPod Nano
+Banknote
+Mario & Sonic at the Olympic Games
+Hero Factory
+Bamboo
+Fillet (cut)
+Stencil
+Winch
+Dogfight
+Treadmill
+Bassoon
+Staffordshire Bull Terrier
+Cardboard
+Epiphone Les Paul
+Compact Cassette
+Gelatin
+White House
+Suitcase
+MX vs. ATV
+Clank (Ratchet & Clank)
+Beach volleyball
+Loadout
+Batter (cooking)
+Zack Fair
+Cliff
+Baggage
+Cream cheese
+Lantern
+Naruto: Clash of Ninja
+Treasure
+Raccoon
+Mini 4WD
+Robotic vacuum cleaner
+Gate
+Ribs (food)
+Oatmeal
+Water filter
+Super Mario Sunshine
+Animal Crossing: City Folk
+Driver's license
+Asus ZenFone
+American black bear
+Little Red Riding Hood
+???
+Stable
+Gashapon
+Need for Speed: Underground
+Dishwasher
+Frying pan
+Schutzhund
+Mario Kart 7
+Disney Infinity
+Saab Automobile
+F-Zero
+Halloween costume
+Thor (Marvel Comics)
+Foam
+Tokyo Ghoul
+Chevrolet Monte Carlo
+Flush toilet
+Axe
+Worms (series)
+Marble
+Driver's education
+Madden NFL 12
+Pressure washing
+Christmas ornament
+Buffalo wing
+Duct (flow)
+Indiana Jones
+Chart
+Yoshi's Island
+Subaru Forester
+Scar (The Lion King)
+Mousse
+Lalaloopsy
+Micropterus
+Gibson SG
+Express train
+Citroën C4
+Submission wrestling
+Broccoli
+Donkey Kong Country 2: Diddy's Kong Quest
+Barrel organ
+Mega Man 2
+Dragon boat
+New Super Mario Bros. U
+Gecko
+Pillow
+Kemenche
+Porsche Cayenne
+???
+Shift 2: Unleashed
+Bomberman
+Dungeons & Dragons
+BeamNG.drive
+AdventureQuest
+Mario Kart 64
+Disc brake
+Bloons Tower Defense
+Forza Motorsport 3
+Guitar Center
+Super Smash Bros. (video game)
+Fiat Uno
+Printed circuit board
+Porcelain
+E-book
+Macaroni
+Lego Friends
+Max Payne 3
+StarCraft II: Heart of the Swarm
+Medal of Honor: Warfighter
+Kamaz
+Air France
+Porsche Carrera GT
+Black Rock Shooter
+Rosary
+Halo Wars
+Car dealership
+Toys "R" Us
+Total War: Rome II
+Need for Speed: ProStreet
+Mansion
+Cheetah
+Marshmallow
+Shorts
+Unturned
+Charango
+Lithium polymer battery
+Sea turtle
+Vatican City
+Starbucks
+Emergency vehicle lighting
+Volkswagen Golf Mk1
+Lupin the Third
+Pearl
+Wii Sports
+Hero
+Chrysler 300
+GMC (automobile)
+Charm bracelet
+Kamen Rider Battle: Ganbaride
+Ys (series)
+Asus Eee Pad Transformer
+BMW 5 Series (E60)
+Ford Mustang SVT Cobra
+Autocross
+Royal icing
+Laboratory
+Peugeot 206
+Maltese (dog)
+Soulcalibur IV
+Wardrobe
+Garlic
+Tugboat
+Luke Skywalker
+Electronic circuit
+Coat (clothing)
+Passenger
+???
+Cactus
+Ford Crown Victoria
+Elfen Lied
+Circular saw
+Radha
+Welsh Corgi
+Eiffel Tower
+Softail
+Bajo sexto
+Lobster
+Colt (horse)
+Solar eclipse
+Greyhound
+Pepsi
+Black Widow (Natasha Romanova)
+Virtua Fighter
+Filly
+Canning
+Fat
+Goth subculture
+Slow cooker
+Lightning (Final Fantasy)
+Water polo
+Apple pie
+Inkjet printing
+Mercedes-Benz SLK-Class
+Bandsaw
+Cammy
+Fight Night (EA video game series)
+Tortoise
+Multicooker
+Ferret
+Dipping sauce
+Circle
+Rocket launch
+Pembroke Welsh Corgi
+Cold porcelain
+Battlefield Play4Free
+ThinkPad
+BMW X6
+???
+Sony Xperia Z
+Selfie
+Mahjong
+Cherry
+IPod Touch (5th generation)
+Colin McRae: Dirt 2
+Tekken 5
+Shawl
+Ultron
+Guitar pick
+Elk
+Sunrise
+Amusement arcade
+Hammock
+Decoupage
+Mug
+Sander
+Autogyro
+Woodchipper
+Texas Instruments
+Baby Alive
+Tarantula
+Shrub
+Donkey Kong (video game)
+Coating
+Steirische Harmonika
+Racing wheel
+Raphael (Teenage Mutant Ninja Turtles)
+Bank
+Opel Vectra
+Skull
+Sand art and play
+Birth
+Lasagne
+Infinity Ward
+Philippine cuisine
+Custard
+Lettuce
+Megami Tensei
+Flappy Bird
+Sleeping Dogs (video game)
+Fender Jazz Bass
+Devil Kings
+Blouse
+Notebook
+Aloe vera
+Funko
+Lelouch Lamperouge
+Macramé
+Casserole
+Capacitor
+I Wanna Be the Guy
+Hose
+Subaru Legacy
+Star Citizen
+Sabian
+Ventriloquism
+Call of Duty (video game)
+Kindle Fire
+Starfire (Koriand'r)
+Zeus
+Microscope
+Basket
+Coyote
+Bart Simpson
+Volvo FH
+Spinnerbait
+Honda CR-V
+Sony Xperia Z1
+Satan
+Mercedes-Benz Sprinter
+Team roping
+Jeep Cherokee (XJ)
+Friendship bracelet
+Leonardo (Teenage Mutant Ninja Turtles)
+Single track (mountain biking)
+Chickpea
+Vegetable carving
+???
+Spark plug
+Akita (dog)
+Canoeing
+Recumbent bicycle
+Boom Beach
+Puppetry
+Sport stacking
+Kendama
+Punching bag
+Staples Center
+Marvel vs. Capcom 2: New Age of Heroes
+Apple TV
+Davul
+Scratchcard
+Disgaea
+Larva
+Used car
+DmC: Devil May Cry
+Kyo Kusanagi
+Mega Man (video game)
+K'Nex
+Burger King
+Dungeon crawl
+Pro Evolution Soccer 2009
+Blueberry
+Village
+Convenience store
+Golf cart
+BMW M6
+Fiber
+Resistance (series)
+Picture frame
+Trouble in Terrorist Town
+Volkswagen Type 2
+Domestic pig
+Grand Tourer Injection
+Alucard (Hellsing)
+Aerith Gainsborough
+Batmobile
+Gummi candy
+Cauliflower
+Marlin
+Gold medal
+Shin Megami Tensei: Persona 3
+Table football
+Shikamaru Nara
+Truggy
+Ford Explorer
+Chevrolet Cruze
+American Airlines
+Jupiter
+Galaxy Nexus
+KFC
+Spec Ops: The Line
+Rigs of Rods
+EA Sports UFC
+Plastic bottle
+Hubble Space Telescope
+Barn
+Hand
+Star Wars: Battlefront (2004 video game)
+Digimon Masters
+Gibson ES-335
+Waffle
+Paper model
+Ressha Sentai ToQger
+Gas tungsten arc welding
+Pavement (architecture)
+Sonic & Sega All-Stars Racing
+???
+Palace
+Stealth game
+God of War (2005 video game)
+Mazda6
+Dragon Age II
+Warhammer Online: Age of Reckoning
+Switch
+Grizzly bear
+???
+H.A.V.E. Online
+Lowlands (festival)
+Wok
+Window blind
+Nokia N8
+Android Wear
+V10 engine
+Toyota Tundra
+Marble (toy)
+Alligator
+Screencast
+Range Rover Sport
+Moose
+Polo
+Laminate flooring
+BVE Trainsim
+Baby sling
+Garage door
+Compact car
+Dishonored
+Parrot AR.Drone
+Giraffe
+Need for Speed Rivals
+McLaren 12C
+Pork ribs
+Track cycling
+Don't Starve
+Marvel: Avengers Alliance
+Popeye
+Ford Mondeo
+HTC One (M7)
+Pyramid
+Asphalt
+Beetle
+Canon EOS 600D
+Oldsmobile Cutlass
+Suzuki GSX-R750
+Audi A8
+World of Warcraft: The Burning Crusade
+Homing pigeon
+NHL 15
+Touring motorcycle
+Goblin
+Nissan 370Z
+Metro: Last Light
+Skylanders: Giants
+Ran Online
+Gear
+Mercedes-Benz G-Class
+Travian
+Burnout Paradise
+Tag team
+Electric motorcycles and scooters
+Kazuya Mishima
+Serious Sam
+Nexus 7 (2013)
+Super Paper Mario
+Doodle
+Gelatin dessert
+Andalusian horse
+Warrior
+Ferrari 360
+DVD player
+WildStar (video game)
+Hyundai Genesis
+Chutney
+Pizzica
+Dead Rising 2
+Potter's wheel
+Yoda
+Cylinder (engine)
+M. Bison
+Metal Gear Solid: Peace Walker
+Masonry
+Edward Elric
+Split (gymnastics)
+Mario Kart DS
+Ghost Rider
+Grand Theft Auto: Episodes from Liberty City
+F1 2012 (video game)
+Cookie Monster
+Red hair
+Nami (One Piece)
+Canon EF lens mount
+Finger
+Asteroid
+Nissan Navara
+Riddler
+Traffic light
+Nikon Coolpix series
+Dragonica
+Broth
+Metal Gear Solid 2: Sons of Liberty
+Samsung Galaxy Y
+Wedding cake
+Half-pipe
+Gothic II
+Vehicle horn
+Motor oil
+Credit card
+Resident Evil 2
+British Airways
+Great Dane
+Stain
+Super Mario 3D World
+Yamaha YZ125
+Atari 2600
+Rover (space exploration)
+Cayman
+Ragdoll
+Basement
+Betta
+Mobile home
+Heroes of Might and Magic
+Photograph
+Wreath
+Universe of The Legend of Zelda
+Lamborghini Diablo
+Albus Dumbledore
+BlackBerry Bold
+Prototype 2
+Soybean
+Hurdling
+Spock
+Sony Xperia Z2
+Monopoly (game)
+Fruit preserves
+SimCity (2013 video game)
+Cutlet
+Volkswagen Touareg
+Aerosol paint
+Risotto
+Toyota 4Runner
+Driveclub
+Moshing
+Total War: Shogun 2
+Elf
+Hot tub
+President
+NHL 13
+Rudolph the Red-Nosed Reindeer
+Bugs Bunny
+Mario & Luigi: Superstar Saga
+Tulip
+Paper Mario: The Thousand-Year Door
+Hammer
+EarthBound
+Meta Knight
+La Tale
+Shadow of the Colossus
+GLaDOS
+Hunting dog
+BioShock 2
+Supercars Championship
+Orbit
+God of War: Ascension
+Bloons
+Ney
+Toyota MR2
+Cam
+???
+Zoom lens
+H&M
+Hovercraft
+Sanshin
+Instant noodle
+Luigi's Mansion
+Tales of Vesperia
+Dekotora
+???
+Talking Tom and Friends
+Baseball glove
+Ale
+Meringue
+Canon EOS 7D
+Shaolin Kung Fu
+Hawk
+Donkey Kong Country Returns
+The Salvation Army
+Brown trout
+Sugarcane
+Cake pop
+Suzuki Bandit series
+Green tea
+Warehouse
+Appalachian dulcimer
+Kermit the Frog
+Unicorn
+Fountain pen
+Acer Iconia
+Master System
+Robocraft
+Merlin
+Sweet potato
+Alice's Adventures in Wonderland
+Solar flare
+DigiTech
+Saturn
+Flash (comics)
+Reindeer
+Justice League
+Line Rider
+Runes of Magic
+Chevrolet Suburban
+Michael Myers (Halloween)
+Need for Speed: Undercover
+Wand
+Chevrolet Malibu
+Coal
+Antena 3 (Spain)
+Driver: San Francisco
+Font
+Stingray
+Thermostat
+Toph Beifong
+Vert ramp
+Ridge Racer
+Goat Simulator
+Lineage (video game)
+CNBC
+Juri (Street Fighter)
+TARDIS
+Pigeon racing
+Lap steel guitar
+Shovel
+Mosaic
+Monster Retsuden Oreca Battle
+Pair skating
+Wallpaper
+The Simpsons: Tapped Out
+The Elder Scrolls III: Morrowind
+Padel (sport)
+Fender (vehicle)
+Furnace
+Nissan Altima
+Cornet
+Škoda Fabia
+Lockheed Martin F-35 Lightning II
+Electribe
+Alesis
+Motorola Razr
+Halo: Combat Evolved Anniversary
+Darksiders
+Neo Geo (system)
+Snail
+Milking
+Pluto (Disney)
+Peanut
+Verona Arena
+Chubby Bunny
+Jerry Mouse
+Corvette Stingray (concept car)
+Cigarette
+Cube World
+???
+Cybertron
+Dacia Duster
+Pastel
+Transformer
+Split screen (computer graphics)
+Sukhoi Su-27
+Gabrielle (Xena: Warrior Princess)
+Opel Kadett
+Nokia Lumia 920
+Twin-turbo
+Jiraiya (Naruto)
+The Legend of Zelda: A Link to the Past
+Crappie
+Rechargeable battery
+???
+Super Mario 3D Land
+???
+DragonFable
+Aragorn
+Crash Bandicoot 2: Cortex Strikes Back
+Southwest Airlines
+Multi-tool
+Passport
+Porsche Panamera
+Airship
+Tuxedo Mask
+Tom Clancy's Ghost Recon: Future Soldier
+Melty Blood
+Beam (structure)
+Gas metal arc welding
+Audi Q7
+Bell pepper
+Chewing gum
+Drinking water
+Heat pump
+Kenshiro
+Patrick Drake and Robin Scorpio
+Miniature wargaming
+Kawasaki Ninja 650R
+Captain Falcon
+J-Stars Victory VS
+Imperishable Night
+Citrus
+Drift trike
+Optical illusion
+Command & Conquer: Red Alert 3
+Suzuka Circuit
+Mayonnaise
+Quake III Arena
+Keychain
+God Mode
+Ford Bronco
+Crocodilia
+Black and white
+Llanero
+Monorail
+Nova
+G.I. Joe
+S.T.A.L.K.E.R.: Call of Pripyat
+Perfect Cherry Blossom
+Wine tasting
+Olive
+Ultra Series
+Beat 'em up
+Jellyfish
+Lego Legends of Chima
+Sauna
+Tom Clancy's Splinter Cell: Blacklist
+Starscream
+Aang
+Misty (Pokémon)
+IPad Air
+Ice pop
+Lute
+Jigsaw puzzle
+Baritone saxophone
+BMW Z4
+Mana (series)
+Motorized bicycle
+Dalmatian (dog)
+Bose Corporation
+Burton Snowboards
+Kingdom Hearts: Chain of Memories
+Mass Rapid Transit (Singapore)
+Boombox
+Napkin
+Chimpanzee
+Guitar Hero: Metallica
+Radar detector
+Honda NSX
+Empire: Total War
+Darts
+Light fixture
+Super Mario Bros. 2
+Temple Run
+Kristoff (Disney)
+Adrenalyn XL
+Tatra (company)
+Mini-Z
+Tin can
+Market garden
+Mercedes-Benz Actros
+Hug
+Whipped cream
+Wasp
+Oni
+Princess Daisy
+Constellation
+HTC One X
+Fender Precision Bass
+Prawn
+Christmas card
+Handbell
+Coconut milk
+Toshiba Satellite
+Riven
+Referee
+Dragon's Dogma
+Dalek
+Folding bicycle
+2 Days
+Kimono
+Seiko
+Hippopotamus
+Resident Evil: Revelations
+Billboard (magazine)
+Padlock
+Butterfly stroke
+Mashed potato
+Yuan Zai (giant panda)
+Aurora
+Mop
+Tubing (recreation)
+Clothes iron
+Order & Chaos Online
+Zebra
+Crème caramel
+Warhammer 40,000: Dawn of War
+Tom Clancy's Splinter Cell: Conviction
+Wakfu
+Stitch (Lilo & Stitch)
+Calf
+Cars 2 (video game)
+Crayfish
+Engagement ring
+Infamous Second Son
+Jukebox
+Biryani
+DJ Hero
+Super GT
+Chameleon
+Oyster
+Warcraft III: The Frozen Throne
+Dynasty Warriors 7
+Postage stamp
+Derek Shepherd
+Plotter
+Amnesia: The Dark Descent
+Jinn
+Rayman Legends
+Tinker Bell
+Patchwork
+Doom 3
+Wat
+Paiste
+Mercedes-Benz CLS-Class
+Liquid
+GameTrailers
+Pep squad
+Clam
+SaGa (series)
+Nollie
+Company of Heroes
+Green Arrow
+Naruto Uzumaki
+DeWalt
+Putter
+Family
+Transistor
+SOCOM (series)
+Pea
+Social media
+Aliens vs. Predator (2010 video game)
+HTC HD2
+Ducati Monster
+Aggressive inline skating
+Maserati GranTurismo
+PortAventura World
+Lego Batman: The Videogame
+Energy drink
+Turban
+Pokémon Yellow
+Alaskan Malamute
+Monica's Gang
+Suzuki Vitara
+Black Desert Online
+Zara (retailer)
+Just Dance 2015
+Maid Sama!
+Disguise
+Kidney
+Water well
+Farmer
+Toyota RAV4
+Night
+DJMax
+Richter-tuned harmonica
+Real Racing 3
+Solid Snake
+United States dollar
+F1 2010 (video game)
+Samsung Galaxy Ace
+Trials Evolution
+Cadillac CTS
+Daihatsu
+Balcony
+Xperia Play
+Rookie
+Timing belt (camshaft)
+Monster Energy
+Ork (Warhammer 40,000)
+Toyota JZ engine
+Drive-through
+Spektrum RC
+Hyundai Sonata
+Chinchilla
+Wii Sports Resort
+Interchange (road)
+Whitewater slalom
+Ticket (admission)
+Bayonetta
+Salsa (sauce)
+PlayStation All-Stars Battle Royale
+Lego Minecraft
+???
+Mule
+Starbound
+Scissors
+Asparagus
+Sony NEX-5
+Electrical connector
+Rayquaza
+Eight-ball
+Steel-string acoustic guitar
+Strap
+Times Square
+Bus driver
+SEAT Ibiza
+Converse (shoe company)
+Atlantic bluefin tuna
+Mercedes-Benz W124
+???
+Goggles
+Kawasaki Z1000
+Shrimp and prawn as food
+Garnier
+Semi-trailer
+Cod
+Carpet cleaning
+Lost Planet
+Sonic the Hedgehog CD
+Final Fantasy V
+F1 2013 (video game)
+Modelling clay
+Audi Sportback concept
+WWE All Stars
+Mitsubishi Outlander
+Punch-Out!!
+Disney Infinity: Marvel Super Heroes
+Mulch
+Willy Wonka
+Dead Space 3
+Eurofighter Typhoon
+H1Z1: Just Survive
+Fakie
+Super Mario RPG
+Dance Central 3
+Puppet
+Cursor (user interface)
+Prince of Persia: Warrior Within
+Ultimate Mortal Kombat 3
+Macross
+Upholstery
+The Binding of Isaac (video game)
+Deathstroke
+The King of Fighters '98
+Dragon Ball Z: Battle of Z
+Theatre organ
+Valve Corporation
+Age of Conan
+GameStop
+Unreal Tournament
+Metroid Prime
+Annie (musical)
+Cinderella (Disney character)
+Eric Cartman
+The Prince of Tennis
+Kia Sportage
+Vase
+Nightwing
+Wing
+Gouken
+Loft
+Ferris wheel
+Newspaper
+Cash
+A Certain Magical Index
+Pretty Rhythm
+Marionette
+Swing (seat)
+He-Man
+Cook (profession)
+Bentley Continental GT
+Shaman King
+Hakuōki
+Essential oil
+Balalaika
+Baja 1000
+Hummingbird
+PSA HDi engine
+Nissan Sentra
+???
+Infamous (video game)
+Game Boy Color
+343 Industries
+Six Flags Magic Mountain
+Woozworld
+It's a Small World
+Star Fox 64
+Xenoblade Chronicles
+TurboGrafx-16
+Tesla coil
+HTC Evo 4G
+Super Metroid
+Label
+Gothic (video game)
+Samsung Galaxy Gear
+???
+Viola caipira
+Space Engineers
+Yamaha MT-09
+Mortal Kombat: Armageddon
+Angry Birds Star Wars
+Aerography (arts)
+Python (genus)
+Hyundai Elantra
+MG Cars
+Tesla Model S
+Castlevania: Symphony of the Night
+Body armor
+Bone
+Tekken 5: Dark Resurrection
+Kimchi
+Wedding invitation
+Porsche 930
+Whey protein
+Winery
+Honda Integra DC5
+Hatter (Alice's Adventures in Wonderland)
+Double Dutch (jump rope)
+Cort Guitars
+One-man band
+Dentures
+Tupperware
+The Lion King (musical)
+BlackBerry Z10
+Kingdom Hearts III
+Zipper
+Leaf
+Samsung Galaxy Note 10.1
+Bansuri
+BMW 5 Series (F10)
+Australian Shepherd
+Crash Bandicoot: Warped
+Pou (video game)
+Tilapia
+Peugeot 205
+AC Cobra
+Tin whistle
+Tooth brushing
+Battlefield 1942
+Virginia Tech
+Quarry
+Amphibious ATV
+Dome
+Portable stove
+Sound system (Jamaican)
+Suikoden
+Lunar eclipse
+Tiramisu
+Inazuma Eleven GO (video game)
+Nissan 300ZX
+Neverwinter (video game)
+Axle
+Altaïr Ibn-La'Ahad
+Radiator
+Resident Evil (2002 video game)
+Prince of Persia: The Sands of Time
+Crop circle
+Rhinoceros
+???
+Bookcase
+Common quail
+The Hunger Games
+Mercedes-Benz A-Class
+Sarah Walker (Chuck)
+Cinnamon
+Hiru TV
+Bread roll
+Magician (fantasy)
+Lotion
+Killzone 3
+Cadillac Escalade
+Silhouette
+Swan
+Lemonade
+Trabant
+Mojito
+Fossil
+Macy's
+Silk
+Puma SE
+Nissan Maxima
+Battlefield 2142
+Twisted Metal
+Olive oil
+Wii Remote
+Universal Studios Hollywood
+Berserk (manga)
+Wellington boot
+Tomb Raider: Anniversary
+Almond
+Audi RS 6
+Ladder
+Fire Emblem Awakening
+Stained glass
+Tape recorder
+Emerald
+Ford Fusion (Americas)
+Iguana
+Might and Magic
+Pluto
+Mazda Raceway Laguna Seca
+Air Force 1 (shoe)
+Pub
+Oshun
+Honda K engine
+Nerd
+Renault 5
+F1 2011 (video game)
+Windscreen wiper
+Lex Luthor
+Track racing
+Escalator
+Charlie Brown
+Chauffeur
+Soba
+Window film
+Bowl
+Alarm clock
+Pokémon Mystery Dungeon: Explorers of Time and Explorers of Darkness
+Roomba
+Honda Shadow
+Lightning Returns: Final Fantasy XIII
+LATAM Brasil
+Top
+American Bulldog
+Legoland
+Caterpillar
+Windows Phone 8
+Automated teller machine
+Samsung Galaxy S III Mini
+Portrait photography
+Office
+Para Para
+Hockey stick
+Singapore Airlines
+Volvo S60
+Udon
+Chevrolet K5 Blazer
+Bath & Body Works
+Segway PT
+Castlevania: Lords of Shadow
+Mario Kart: Double Dash
+Mew (Pokémon)
+Walkman
+Mentos
+Jilbāb
+Canter and gallop
+Cinderella
+Skylanders: Trap Team
+Lego Duplo
+Morgan le Fay
+Decal
+Handycam
+Women's Tennis Association
+Yeti
+Multi-valve
+Pokémon Stadium
+Matryoshka doll
+Lexus LFA
+Keirin
+???
+Honda Prelude
+Burrito
+Midna
+Shuriken
+New Super Mario Bros. 2
+Nebula
+BlackBerry PlayBook
+Typography
+Hare
+Mohawk hairstyle
+Onsen
+Jet pack
+Wagon
+Just Dance 3
+Nissan S30
+Noah's Ark
+Ronald McDonald
+Bombardier Dash 8
+Raspberry
+Hair dryer
+The Simpsons: Hit & Run
+Still life
+Ice climbing
+Lada Riva
+Port
+Compound bow
+Resident Evil 3: Nemesis
+R2-D2
+Sand animation
+ABS-CBN (television network)
+Leica Camera
+Final Fantasy (video game)
+Arkham Asylum
+Dynasty Warriors 8
+Text messaging
+Nursery (room)
+Donkey Kong 64
+Star Wars Jedi Knight: Jedi Academy
+Typing
+Mapex Drums
+Granado Espada
+Calendar
+UFC Undisputed 3
+Airbag
+DMC World DJ Championships
+Gingerbread
+Rayman Origins
+Lamborghini Reventón
+Trials Fusion
+Mafia (video game)
+Paso Fino
+???
+Sport kite
+Taco Bell
+Envelope
+Mazdaspeed3
+Transformers: Generation 1
+Empanada
+Mega Man 3
+Transformers: Fall of Cybertron
+Rosalina (character)
+Mosquito
+Volkswagen Tiguan
+Metal Gear Solid V: Ground Zeroes
+Marmalade
+Pandeiro
+Miss Saigon
+Yosemite National Park
+Dutch Warmblood
+Pre-flight safety demonstration
+Citroën Saxo
+Mack Trucks
+Medley swimming
+???
+Spindle (tool)
+Greek cuisine
+Hyundai Santa Fe
+Chili con carne
+Poster
+Kawasaki Ninja 300
+Baby food
+Grand Theft Auto (Game Boy Advance)
+Sim racing
+Chromebook
+Peter Griffin
+Stainless steel
+Beverage can
+Pixie cut
+Chevrolet SS (concept car)
+Chokehold
+Bullion
+Super Mario Kart
+The Sims FreePlay
+Giant Bicycles
+Sgt. Frog
+Age of Empires II
+Abadá
+Kingdom Hearts HD 1.5 Remix
+Blackjack
+Canon EOS 60D
+Filling station
+Plywood
+Pheasant
+Wilson Sporting Goods
+Comb
+Lighthouse
+Rock and Roll Hall of Fame
+Tōshirō Hitsugaya
+Tales of the Abyss
+Maze
+Resident Evil: Operation Raccoon City
+Cimbalom
+???
+Monkey Island (series)
+Civilization V
+Venus
+Peugeot 207
+The Amazing Spider-Man (2012 video game)
+Chrono Cross
+New Balance
+Dassault Rafale
+Daredevil (Marvel Comics character)
+Silent Hill 2
+Beanie (seamed cap)
+Nut (fruit)
+Jill Valentine
+Scion tC
+Percy Jackson
+Lord of the Dance (musical)
+Far Cry (video game)
+Star Wars: The Force Unleashed II
+Memory card
+Motorola Droid
+Skylanders: Spyro's Adventure
+Yamaha DT125
+Audi Q5
+Jaguar
+Jaguar XJ
+Animal Crossing: Wild World
+Cockroach
+Wetsuit
+Funny Car
+FarmVille
+The Sims 3: Pets
+Peel (fruit)
+Melting
+Aurora (Disney character)
+Dry ice
+Star Ocean
+Duke Nukem Forever
+Toribash
+Yamaha YZ250
+Tekken 3
+Orihime Inoue
+Spyro: Year of the Dragon
+Eight-string guitar
+Sonic Riders
+Penny (The Big Bang Theory)
+Honda XR series
+Neodymium magnet toys
+Leatherman
+Maximum Destruction
+Super Mario 64 DS
+Unreal Tournament 3
+Health club
+Chrysler Hemi engine
+The North Face
+CBS News
+Pentium
+Cannon
+London Fashion Week
+Military tactics
+Smallmouth bass
+Leopard gecko
+Top (clothing)
+Fable III
+Panasonic Lumix DMC-GH4
+Sikorsky UH-60 Black Hawk
+Blue Dragon
+Loudspeaker enclosure
+Ōkami
+Tribal Wars
+Hot chocolate
+Beetroot
+???
+Nokia N97
+Blue Exorcist
+???
+Sonic and the Black Knight
+Headscarf
+Plasma display
+Woody Woodpecker
+???
+Beyblade: Shogun Steel
+29er (bicycle)
+QR code
+Dyson (company)
+Yanmar
+Gladiator
+Nissan Pathfinder
+Nissan X-Trail
+Autofocus
+King Dedede
+Zoo Tycoon 2
+Wheat tortilla
+Team Rocket
+Classical ballet
+New York City Police Department
+Heihachi Mishima
+Crochet hook
+Pencil case
+Gods Eater Burst
+???
+DS 3
+Periodic table
+General Electric
+Nissan Juke
+Lollipop
+Jaguar F-Type
+MechWarrior Online
+Dodge Neon SRT-4
+Fried egg
+Revell
+Indoor soccer
+Gratin
+Punisher
+Washburn Guitars
+Caster board
+Eldar (Warhammer 40,000)
+Final Fantasy Type-0
+NBA 2K10
+The Lord of the Rings: The Battle for Middle-earth II
+Texas Longhorns
+3D television
+Scorpion
+Warhammer 40,000: Dawn of War II
+Burpee (exercise)
+The Order: 1886
+Poptropica
+Tomb Raider: Legend
+Pelmeni
+Bánh
+PriPara
+Legacy of Kain
+Bowser Jr.
+Yonex
+Humanoid robot
+Sony Ericsson Xperia X10
+Rain gutter
+FIFA Street (2012 video game)
+Castle Crashers
+Meteoroid
+Macaroni and cheese
+Sega CD
+Mac Mini
+Tales of Xillia
+Sonic Lost World
+Orphanage
+Siku Toys
+Lego Batman 3: Beyond Gotham
+Daenerys Targaryen
+Orangutan
+Town
+Command & Conquer: Generals
+Samurai Shodown
+ZX Spectrum
+Quake Live
+Weighing scale
+Dead Frontier
+Wolfenstein: The New Order
+Colin McRae: Dirt
+Square dance
+Assassin's Creed Rogue
+Airboat
+Uncharted: Drake's Fortune
+Diddy Kong
+Yamaha Motif
+Theremin
+Rilakkuma
+Tie-dye
+Flip-flops
+Cylinder
+Gothic 3
+Unreal (1998 video game)
+Beyond: Two Souls
+Umbrella
+Dream Club
+Gradius
+Nexus One
+Nokia N900
+Tamagotchi
+Husband
+Sleeping bag
+Look-alike
+Papaya
+Mother 3
+The Beatles: Rock Band
+Prince of Persia: The Two Thrones
+???
+Darth Maul
+Knife sharpening
+Meteor shower
+Flugelhorn
+One Piece: Pirate Warriors
+Asterix
+Talk box
+With Your Destiny
+Alan Wake
+Barcode
+Recurve bow
+Diaper bag
+Ferrari F12berlinetta
+Taskbar
+Mortar (masonry)
+Toner (skin care)
+Freddy Krueger
+Marriott International
+Mass Effect (video game)
+Hawkeye (comics)
+Killing Floor (video game)
+Chibiusa
+Screenshot
+Pear
+Injury
+Kia Sorento
+Shredder (Teenage Mutant Ninja Turtles)
+Lifeguard
+Kei car
+Fight Night Champion
+Terra (comics)
+Gamblerz
diff --git a/mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt b/mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt
new file mode 100644
index 0000000..12ed2cb
--- /dev/null
+++ b/mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt
@@ -0,0 +1,178 @@
+input_side_packet: "input_sequence_example_path"
+input_side_packet: "input_video_path"
+input_side_packet: "output_video_path"
+input_side_packet: "segment_size"
+input_side_packet: "overlap"
+
+node {
+  calculator: "LocalFileContentsCalculator"
+  input_side_packet: "FILE_PATH:input_sequence_example_path"
+  output_side_packet: "CONTENTS:input_sequence_example"
+}
+
+node {
+  calculator: "StringToSequenceExampleCalculator"
+  input_side_packet: "STRING:input_sequence_example"
+  output_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+}
+
+node {
+  calculator: "UnpackMediaSequenceCalculator"
+  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
+  output_stream: "FLOAT_FEATURE_RGB:rgb_feature_vector"
+  output_stream: "FLOAT_FEATURE_AUDIO:audio_feature_vector"
+}
+
+node {
+  calculator: "ConcatenateFloatVectorCalculator"
+  input_stream: "rgb_feature_vector"
+  input_stream: "audio_feature_vector"
+  output_stream: "feature_vector"
+}
+
+node {
+  calculator: "VectorFloatToTensorCalculator"
+  input_stream: "feature_vector"
+  output_stream: "feature_tensor"
+}
+
+node {
+  calculator: "StringToInt32Calculator"
+  input_side_packet: "segment_size"
+  output_side_packet: "segment_size_int"
+}
+
+node {
+  calculator: "StringToInt32Calculator"
+  input_side_packet: "overlap"
+  output_side_packet: "overlap_int"
+}
+
+node {
+  calculator: "LappedTensorBufferCalculator"
+  input_stream: "feature_tensor"
+  output_stream: "lapped_feature_tensor"
+  input_side_packet: "BUFFER_SIZE:segment_size_int"
+  input_side_packet: "OVERLAP:overlap_int"
+  node_options: {
+    [type.googleapis.com/mediapipe.LappedTensorBufferCalculatorOptions] {
+      add_batch_dim_to_tensors: true
+    }
+  }
+}
+
+node {
+  calculator: "SidePacketToStreamCalculator"
+  input_side_packet: "segment_size_int"
+  output_stream: "AT_ZERO:segment_size_int_stream"
+}
+
+node {
+  calculator: "VectorIntToTensorCalculator"
+  input_stream: "SINGLE_INT:segment_size_int_stream"
+  output_stream: "TENSOR_OUT:segment_size_tensor"
+}
+
+node {
+  calculator: "PacketClonerCalculator"
+  input_stream: "segment_size_tensor"
+  input_stream: "lapped_feature_tensor"
+  output_stream: "synced_segment_size_tensor"
+}
+
+node {
+  calculator: "TensorFlowSessionFromSavedModelCalculator"
+  output_side_packet: "SESSION:session"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowSessionFromSavedModelCalculatorOptions]: {
+      saved_model_path: "/tmp/mediapipe/saved_model"
+    }
+  }
+}
+
+node: {
+  calculator: "TensorFlowInferenceCalculator"
+  input_side_packet: "SESSION:session"
+  input_stream: "NUM_FRAMES:synced_segment_size_tensor"
+  input_stream: "RGB_AND_AUDIO:lapped_feature_tensor"
+  output_stream: "PREDICTIONS:prediction_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
+      batch_size: 32
+    }
+  }
+}
+
+node {
+  calculator: "TensorToVectorFloatCalculator"
+  input_stream: "prediction_tensor"
+  output_stream: "prediction_vector"
+}
+
+node {
+  calculator: "TopKScoresCalculator"
+  input_stream: "SCORES:prediction_vector"
+  output_stream: "TOP_K_INDEXES:top_k_indexes"
+  output_stream: "TOP_K_SCORES:top_k_scores"
+  output_stream: "TOP_K_LABELS:top_k_labels"
+  node_options: {
+    [type.googleapis.com/mediapipe.TopKScoresCalculatorOptions]: {
+      top_k: 3
+      label_map_path: "mediapipe/graphs/youtube8m/label_map.txt"
+    }
+  }
+}
+
+node {
+  calculator: "OpenCvVideoDecoderCalculator"
+  input_side_packet: "INPUT_FILE_PATH:input_video_path"
+  output_stream: "VIDEO:input_video"
+  output_stream: "VIDEO_PRESTREAM:input_video_header"
+}
+
+node {
+  calculator: "LabelsToRenderDataCalculator"
+  input_stream: "LABELS:top_k_labels"
+  input_stream: "SCORES:top_k_scores"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  output_stream: "RENDER_DATA:render_data"
+  node_options: {
+    [type.googleapis.com/mediapipe.LabelsToRenderDataCalculatorOptions]: {
+      color { r: 255 g: 0 b: 0 }
+      color { r: 0 g: 255 b: 0 }
+      color { r: 0 g: 0 b: 255 }
+      thickness: 2.0
+      font_height_px: 20
+      max_num_labels: 3
+      location: TOP_LEFT
+    }
+  }
+}
+
+node {
+  calculator: "PacketClonerCalculator"
+  input_stream: "render_data"
+  input_stream: "input_video"
+  output_stream: "synchronized_render_data"
+}
+
+node {
+  calculator: "AnnotationOverlayCalculator"
+  input_stream: "IMAGE:input_video"
+  input_stream: "synchronized_render_data"
+  output_stream: "IMAGE:output_video"
+}
+
+node {
+  calculator: "OpenCvVideoEncoderCalculator"
+  input_stream: "VIDEO:output_video"
+  input_stream: "VIDEO_PRESTREAM:input_video_header"
+  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
+  node_options: {
+    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
+      codec: "avc1"
+      video_format: "mp4"
+    }
+  }
+}
+
diff --git a/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt b/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt
new file mode 100644
index 0000000..38a0257
--- /dev/null
+++ b/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt
@@ -0,0 +1,139 @@
+input_side_packet: "desired_segment_size"
+input_side_packet: "record_index"
+input_side_packet: "tfrecord_path"
+output_side_packet: "yt8m_id"
+output_stream: "annotation_summary"
+
+node {
+  calculator: "StringToInt32Calculator"
+  input_side_packet: "record_index"
+  output_side_packet: "record_index_int"
+}
+
+node {
+  calculator: "StringToInt32Calculator"
+  input_side_packet: "desired_segment_size"
+  output_side_packet: "desired_segment_size_int"
+}
+
+node {
+  calculator: "TFRecordReaderCalculator"
+  input_side_packet: "TFRECORD_PATH:tfrecord_path"
+  input_side_packet: "RECORD_INDEX:record_index_int"
+  output_side_packet: "SEQUENCE_EXAMPLE:yt8m_sequence_example"
+}
+
+node {
+  calculator: "UnpackYt8mSequenceExampleCalculator"
+  input_side_packet: "YT8M_SEQUENCE_EXAMPLE:yt8m_sequence_example"
+  input_side_packet: "DESIRED_SEGMENT_SIZE:desired_segment_size_int"
+  output_side_packet: "YT8M_ID:yt8m_id"
+  output_side_packet: "SEGMENT_SIZE:segment_size"
+  output_side_packet: "LAPPED_TENSOR_BUFFER_CALCULATOR_OPTIONS:lapped_tensor_buffer_calculator_options"
+  output_stream: "QUANTIZED_RGB_FEATURE:quantized_rgb_feature"
+  output_stream: "QUANTIZED_AUDIO_FEATURE:quantized_audio_feature"
+}
+
+node {
+  calculator: "DequantizeByteArrayCalculator"
+  input_stream: "ENCODED:quantized_rgb_feature"
+  output_stream: "FLOAT_VECTOR:rgb_feature_vector"
+  node_options: {
+    [type.googleapis.com/mediapipe.DequantizeByteArrayCalculatorOptions]: {
+      max_quantized_value: 2
+      min_quantized_value: -2
+    }
+  }
+}
+
+node {
+  calculator: "DequantizeByteArrayCalculator"
+  input_stream: "ENCODED:quantized_audio_feature"
+  output_stream: "FLOAT_VECTOR:audio_feature_vector"
+  node_options: {
+    [type.googleapis.com/mediapipe.DequantizeByteArrayCalculatorOptions]: {
+      max_quantized_value: 2
+      min_quantized_value: -2
+    }
+  }
+}
+
+node {
+  calculator: "ConcatenateFloatVectorCalculator"
+  input_stream: "rgb_feature_vector"
+  input_stream: "audio_feature_vector"
+  output_stream: "feature_vector"
+}
+
+node {
+  calculator: "VectorFloatToTensorCalculator"
+  input_stream: "feature_vector"
+  output_stream: "feature_tensor"
+}
+
+node {
+  calculator: "LappedTensorBufferCalculator"
+  input_stream: "feature_tensor"
+  input_side_packet: "CALCULATOR_OPTIONS:lapped_tensor_buffer_calculator_options"
+  output_stream: "lapped_feature_tensor"
+}
+
+node {
+  calculator: "SidePacketToStreamCalculator"
+  input_side_packet: "segment_size"
+  output_stream: "AT_ZERO:segment_size_int_stream"
+}
+
+node {
+  calculator: "VectorIntToTensorCalculator"
+  input_stream: "SINGLE_INT:segment_size_int_stream"
+  output_stream: "TENSOR_OUT:segment_size_tensor"
+}
+
+node {
+  calculator: "PacketClonerCalculator"
+  input_stream: "segment_size_tensor"
+  input_stream: "lapped_feature_tensor"
+  output_stream: "synced_segment_size_tensor"
+}
+
+node {
+  calculator: "TensorFlowSessionFromSavedModelCalculator"
+  output_side_packet: "SESSION:session"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowSessionFromSavedModelCalculatorOptions]: {
+      saved_model_path: "/tmp/mediapipe/saved_model"
+    }
+  }
+}
+
+node: {
+  calculator: "TensorFlowInferenceCalculator"
+  input_side_packet: "SESSION:session"
+  input_stream: "NUM_FRAMES:synced_segment_size_tensor"
+  input_stream: "RGB_AND_AUDIO:lapped_feature_tensor"
+  output_stream: "PREDICTIONS:prediction_tensor"
+  node_options: {
+    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
+      batch_size: 32
+    }
+  }
+}
+
+node {
+  calculator: "TensorToVectorFloatCalculator"
+  input_stream: "prediction_tensor"
+  output_stream: "prediction_vector"
+}
+
+node {
+  calculator: "TopKScoresCalculator"
+  input_stream: "SCORES:prediction_vector"
+  output_stream: "SUMMARY:annotation_summary"
+  node_options: {
+    [type.googleapis.com/mediapipe.TopKScoresCalculatorOptions]: {
+      top_k: 9
+      label_map_path: "mediapipe/graphs/youtube8m/label_map.txt"
+    }
+  }
+}
diff --git a/mediapipe/modules/README.md b/mediapipe/modules/README.md
new file mode 100644
index 0000000..12ec103
--- /dev/null
+++ b/mediapipe/modules/README.md
@@ -0,0 +1,18 @@
+# Modules
+
+Each module (represented as a subfolder) provides subgraphs and corresponding resources (e.g. tflite models) to perform domain-specific tasks (e.g. detect faces, detect face landmarks).
+
+*Modules listed below are already used in some of `mediapipe/graphs` and more graphs are being migrated to use existing and upcoming modules.*
+
+| Module | Description |
+| :--- | :--- |
+| [`face_detection`](face_detection/README.md) | Subgraphs to detect faces. |
+| [`face_geometry`](face_geometry/README.md) | Subgraphs to extract face geometry. |
+| [`face_landmark`](face_landmark/README.md) | Subgraphs to detect and track face landmarks. |
+| [`hand_landmark`](hand_landmark/README.md) | Subgraphs to detect and track hand landmarks. |
+| [`holistic_landmark`](holistic_landmark/README.md) | Subgraphs to detect and track holistic pose which consists of pose, face and hand landmarks. |
+| [`iris_landmark`](iris_landmark/README.md) | Subgraphs to detect iris landmarks. |
+| [`palm_detection`](palm_detection/README.md) | Subgraphs to detect palms/hands. |
+| [`pose_detection`](pose_detection/README.md) | Subgraphs to detect poses. |
+| [`pose_landmark`](pose_landmark/README.md) | Subgraphs to detect and track pose landmarks. |
+| [`objectron`](objectron/README.md) | Subgraphs to detect and track 3D objects. |
diff --git a/mediapipe/modules/face_detection/BUILD b/mediapipe/modules/face_detection/BUILD
new file mode 100644
index 0000000..b1cddeb
--- /dev/null
+++ b/mediapipe/modules/face_detection/BUILD
@@ -0,0 +1,150 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "face_detection_short_range_by_roi_cpu",
+    graph = "face_detection_short_range_by_roi_cpu.pbtxt",
+    register_as = "FaceDetectionShortRangeByRoiCpu",
+    deps = [
+        ":face_detection_short_range_common",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_short_range_by_roi_gpu",
+    graph = "face_detection_short_range_by_roi_gpu.pbtxt",
+    register_as = "FaceDetectionShortRangeByRoiGpu",
+    deps = [
+        ":face_detection_short_range_common",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_short_range_cpu",
+    graph = "face_detection_short_range_cpu.pbtxt",
+    register_as = "FaceDetectionShortRangeCpu",
+    deps = [
+        ":face_detection_short_range_common",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_short_range_gpu",
+    graph = "face_detection_short_range_gpu.pbtxt",
+    register_as = "FaceDetectionShortRangeGpu",
+    deps = [
+        ":face_detection_short_range_common",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_short_range_common",
+    graph = "face_detection_short_range_common.pbtxt",
+    register_as = "FaceDetectionShortRangeCommon",
+    deps = [
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/util:detection_projection_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_full_range_cpu",
+    graph = "face_detection_full_range_cpu.pbtxt",
+    register_as = "FaceDetectionFullRangeCpu",
+    deps = [
+        ":face_detection_full_range_common",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_full_range_gpu",
+    graph = "face_detection_full_range_gpu.pbtxt",
+    register_as = "FaceDetectionFullRangeGpu",
+    deps = [
+        ":face_detection_full_range_common",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_full_range_common",
+    graph = "face_detection_full_range_common.pbtxt",
+    register_as = "FaceDetectionFullRangeCommon",
+    deps = [
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/util:detection_projection_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_short_range_image",
+    graph = "face_detection_short_range_image.pbtxt",
+    register_as = "FaceDetectionShortRangeImage",
+    deps = [
+        ":face_detection_short_range_common",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_full_range_image",
+    graph = "face_detection_full_range_image.pbtxt",
+    register_as = "FaceDetectionFullRangeImage",
+    deps = [
+        ":face_detection_full_range_common",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "face_detection_full_range.tflite",
+        "face_detection_full_range_sparse.tflite",
+        "face_detection_short_range.tflite",
+    ],
+)
diff --git a/mediapipe/modules/face_detection/README.md b/mediapipe/modules/face_detection/README.md
new file mode 100644
index 0000000..17cf27b
--- /dev/null
+++ b/mediapipe/modules/face_detection/README.md
@@ -0,0 +1,8 @@
+# face_detection
+
+Subgraphs|Details
+:--- | :---
+[`FaceDetectionFullRangeCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt)| Detects faces. Works best for faces within 5 meters from the camera. (CPU input, and inference is executed on CPU.)
+[`FaceDetectionFullRangeGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt)| Detects faces. Works best for faces within 5 meters from the camera. (GPU input, and inference is executed on GPU.)
+[`FaceDetectionShortRangeCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt)| Detects faces. Works best for faces within 2 meters from the camera. (CPU input, and inference is executed on CPU.)
+[`FaceDetectionShortRangeGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt)| Detects faces. Works best for faces within 2 meters from the camera. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/face_detection/face_detection_full_range.tflite b/mediapipe/modules/face_detection/face_detection_full_range.tflite
new file mode 100755
index 0000000..98c5c16
Binary files /dev/null and b/mediapipe/modules/face_detection/face_detection_full_range.tflite differ
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_common.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_common.pbtxt
new file mode 100644
index 0000000..937e8be
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_full_range_common.pbtxt
@@ -0,0 +1,102 @@
+# MediaPipe graph performing common processing to detect faces using
+# face_detection_full_range_sparse.tflite model, currently consisting of tensor
+# post processing.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionFullRangeCommon"
+#     input_stream: "TENSORS:detection_tensors"
+#     input_stream: "MATRIX:transform_matrix"
+#     output_stream: "DETECTIONS:detections"
+#   }
+
+type: "FaceDetectionShortRangeCommon"
+
+# Detection tensors. (std::vector<Tensor>)
+input_stream: "TENSORS:detection_tensors"
+
+# A 4x4 row-major-order matrix that maps a point represented in the detection
+# tensors to a desired coordinate system, e.g., in the original input image
+# before scaling/cropping. (std::array<float, 16>)
+input_stream: "MATRIX:transform_matrix"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 1
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_height: 192
+      input_size_width: 192
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 4
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+      interpolated_scale_aspect_ratio: 0.0
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:unfiltered_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 2304
+      num_coords: 16
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 6
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+      x_scale: 192.0
+      y_scale: 192.0
+      h_scale: 192.0
+      w_scale: 192.0
+      min_score_thresh: 0.6
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# Projects the detections from input tensor to the corresponding locations on
+# the original image (input to the graph).
+node {
+  calculator: "DetectionProjectionCalculator"
+  input_stream: "DETECTIONS:filtered_detections"
+  input_stream: "PROJECTION_MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt
new file mode 100644
index 0000000..2350401
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt
@@ -0,0 +1,80 @@
+# MediaPipe graph to detect faces. (CPU input, and inference is executed on
+# CPU.)
+#
+# It is required that "face_detection_full_range_sparse.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionFullRangeCpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:face_detections"
+#   }
+
+type: "FaceDetectionFullRangeCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Converts the input CPU image (ImageFrame) to the multi-backend image type
+# (Image).
+node: {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "IMAGE:multi_backend_image"
+}
+
+# Transforms the input image into a 192x192 tensor while keeping the aspect
+# ratio (what is expected by the corresponding face detection model), resulting
+# in potential letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:multi_backend_image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 192
+      output_tensor_height: 192
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
+      delegate {
+        xnnpack {}
+      }
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionFullRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt
new file mode 100644
index 0000000..703b717
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt
@@ -0,0 +1,80 @@
+# MediaPipe graph to detect faces. (GPU input, and inference is executed on
+# GPU.)
+#
+# It is required that "face_detection_full_range_sparse.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionFullRangeGpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:face_detections"
+#   }
+
+type: "FaceDetectionFullRangeGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Converts the input GPU image (GpuBuffer) to the multi-backend image type
+# (Image).
+node: {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "IMAGE:multi_backend_image"
+}
+
+# Transforms the input image into a 128x128 tensor while keeping the aspect
+# ratio (what is expected by the corresponding face detection model), resulting
+# in potential letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:multi_backend_image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 192
+      output_tensor_height: 192
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
+      #
+      delegate: { gpu { use_advanced_gpu_api: true } }
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionFullRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_image.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_image.pbtxt
new file mode 100644
index 0000000..4e0bc0b
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_full_range_image.pbtxt
@@ -0,0 +1,86 @@
+# MediaPipe graph to detect faces. (GPU/CPU input, and inference is executed on
+# GPU.)
+#
+# It is required that "face_detection_full_range_sparse.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
+# path during execution.
+
+type: "FaceDetectionFullRangeImage"
+
+# Image. (Image)
+input_stream: "IMAGE:image"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Transforms the input image into a 128x128 tensor while keeping the aspect
+# ratio (what is expected by the corresponding face detection model), resulting
+# in potential letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 192
+      output_tensor_height: 192
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      gpu_origin: CONVENTIONAL
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+# TODO: Use GraphOptions to modify the delegate field to be
+# `delegate { xnnpack {} }` for the CPU only use cases.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
+      #
+      delegate: { gpu { use_advanced_gpu_api: true } }
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionFullRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite b/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite
new file mode 100755
index 0000000..9575d8c
Binary files /dev/null and b/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite differ
diff --git a/mediapipe/modules/face_detection/face_detection_short_range.tflite b/mediapipe/modules/face_detection/face_detection_short_range.tflite
new file mode 100755
index 0000000..659bce8
Binary files /dev/null and b/mediapipe/modules/face_detection/face_detection_short_range.tflite differ
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_by_roi_cpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_by_roi_cpu.pbtxt
new file mode 100644
index 0000000..b3adfeb
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_short_range_by_roi_cpu.pbtxt
@@ -0,0 +1,83 @@
+# MediaPipe graph to detect faces. (CPU input, and inference is executed on
+# CPU.)
+#
+# It is required that "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionShortRangeByRoiCpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:roi"
+#     output_stream: "DETECTIONS:face_detections"
+#   }
+
+type: "FaceDetectionShortRangeByRoiCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# ROI (region of interest) within the given image where faces should be
+# detected. (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Converts the input CPU image (ImageFrame) to the multi-backend image type
+# (Image).
+node: {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "IMAGE:multi_backend_image"
+}
+
+# Transforms specified region of image into 128x128 tensor keeping aspect ratio
+# (padding tensor if needed).
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:multi_backend_image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 128
+      output_tensor_height: 128
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionShortRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_by_roi_gpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_by_roi_gpu.pbtxt
new file mode 100644
index 0000000..1bd08e9
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_short_range_by_roi_gpu.pbtxt
@@ -0,0 +1,83 @@
+# MediaPipe graph to detect faces. (CPU input, and inference is executed on
+# CPU.)
+#
+# It is required that "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionShortRangeByRoiGpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:roi"
+#     output_stream: "DETECTIONS:face_detections"
+#   }
+
+type: "FaceDetectionShortRangeByRoiGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# ROI (region of interest) within the given image where faces should be
+# detected. (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Converts the input GPU image (GpuBuffer) to the multi-backend image type
+# (Image).
+node: {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "IMAGE:multi_backend_image"
+}
+
+# Transforms specified region of image into 128x128 tensor keeping aspect ratio
+# (padding tensor if needed).
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:multi_backend_image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 128
+      output_tensor_height: 128
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionShortRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_common.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_common.pbtxt
new file mode 100644
index 0000000..4a6a54f
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_short_range_common.pbtxt
@@ -0,0 +1,103 @@
+# MediaPipe graph performing common processing to detect faces, currently
+# consisting of tensor post processing.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionShortRangeCommon"
+#     input_stream: "TENSORS:detection_tensors"
+#     input_stream: "MATRIX:transform_matrix"
+#     output_stream: "DETECTIONS:detections"
+#   }
+
+type: "FaceDetectionShortRangeCommon"
+
+# Detection tensors. (std::vector<Tensor>)
+input_stream: "TENSORS:detection_tensors"
+
+# A 4x4 row-major-order matrix that maps a point represented in the detection
+# tensors to a desired coordinate system, e.g., in the original input image
+# before scaling/cropping. (std::array<float, 16>)
+input_stream: "MATRIX:transform_matrix"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 4
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_height: 128
+      input_size_width: 128
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 8
+      strides: 16
+      strides: 16
+      strides: 16
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:unfiltered_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 896
+      num_coords: 16
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 6
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+      x_scale: 128.0
+      y_scale: 128.0
+      h_scale: 128.0
+      w_scale: 128.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# Projects the detections from input tensor to the corresponding locations on
+# the original image (input to the graph).
+node {
+  calculator: "DetectionProjectionCalculator"
+  input_stream: "DETECTIONS:filtered_detections"
+  input_stream: "PROJECTION_MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt
new file mode 100644
index 0000000..0db2420
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt
@@ -0,0 +1,78 @@
+# MediaPipe graph to detect faces. (CPU input, and inference is executed on
+# CPU.)
+#
+# It is required that "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionShortRangeCpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:face_detections"
+#   }
+
+type: "FaceDetectionShortRangeCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Converts the input CPU image (ImageFrame) to the multi-backend image type
+# (Image).
+node: {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "IMAGE:multi_backend_image"
+}
+
+# Transforms the input image into a 128x128 tensor while keeping the aspect
+# ratio (what is expected by the corresponding face detection model), resulting
+# in potential letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:multi_backend_image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 128
+      output_tensor_height: 128
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionShortRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt
new file mode 100644
index 0000000..d30644b
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt
@@ -0,0 +1,78 @@
+# MediaPipe graph to detect faces. (CPU input, and inference is executed on
+# CPU.)
+#
+# It is required that "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionShortRangeGpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:face_detections"
+#   }
+
+type: "FaceDetectionShortRangeGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Converts the input GPU image (GpuBuffer) to the multi-backend image type
+# (Image).
+node: {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "IMAGE:multi_backend_image"
+}
+
+# Transforms the input image into a 128x128 tensor while keeping the aspect
+# ratio (what is expected by the corresponding face detection model), resulting
+# in potential letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:multi_backend_image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 128
+      output_tensor_height: 128
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionShortRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_image.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_image.pbtxt
new file mode 100644
index 0000000..a259041
--- /dev/null
+++ b/mediapipe/modules/face_detection/face_detection_short_range_image.pbtxt
@@ -0,0 +1,94 @@
+# MediaPipe graph to detect faces. (GPU/CPU input, and inference is executed on
+# GPU.)
+#
+# It is required that "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceDetectionShortRangeCpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:face_detections"
+#   }
+
+type: "FaceDetectionShortRangeCpu"
+
+# Image. (Image)
+input_stream: "IMAGE:image"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+# Detected faces. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:detections"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Transforms the input image into a 128x128 tensor while keeping the aspect
+# ratio (what is expected by the corresponding face detection model), resulting
+# in potential letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "MATRIX:transform_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 128
+      output_tensor_height: 128
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      gpu_origin: CONVENTIONAL
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+# TODO: Use GraphOptions to modify the delegate field to be
+# `delegate { xnnpack {} }` for the CPU only use cases.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+
+      #
+      delegate: { gpu { use_advanced_gpu_api: true } }
+    }
+  }
+}
+
+# Performs tensor post processing to generate face detections.
+node {
+  calculator: "FaceDetectionShortRangeCommon"
+  input_stream: "TENSORS:detection_tensors"
+  input_stream: "MATRIX:transform_matrix"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/face_geometry/BUILD b/mediapipe/modules/face_geometry/BUILD
new file mode 100644
index 0000000..c1f9967
--- /dev/null
+++ b/mediapipe/modules/face_geometry/BUILD
@@ -0,0 +1,137 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
+load("//mediapipe/framework/tool:mediapipe_graph.bzl", "mediapipe_simple_subgraph")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "face_geometry",
+    graph = "face_geometry.pbtxt",
+    register_as = "FaceGeometry",
+    deps = [
+        ":geometry_pipeline_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_geometry_from_detection",
+    graph = "face_geometry_from_detection.pbtxt",
+    register_as = "FaceGeometryFromDetection",
+    deps = [
+        ":geometry_pipeline_calculator",
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/util:detection_to_landmarks_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_geometry_from_landmarks",
+    graph = "face_geometry_from_landmarks.pbtxt",
+    register_as = "FaceGeometryFromLandmarks",
+    deps = [
+        ":geometry_pipeline_calculator",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "effect_renderer_calculator_proto",
+    srcs = ["effect_renderer_calculator.proto"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+    ],
+)
+
+cc_library(
+    name = "effect_renderer_calculator",
+    srcs = ["effect_renderer_calculator.cc"],
+    deps = [
+        ":effect_renderer_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/formats:image_frame_opencv",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:opencv_imgcodecs",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/port:statusor",
+        "//mediapipe/gpu:gl_calculator_helper",
+        "//mediapipe/gpu:gpu_buffer",
+        "//mediapipe/modules/face_geometry/libs:effect_renderer",
+        "//mediapipe/modules/face_geometry/libs:validation_utils",
+        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
+        "//mediapipe/util:resource_util",
+        "@com_google_absl//absl/types:optional",
+    ],
+    alwayslink = 1,
+)
+
+mediapipe_proto_library(
+    name = "env_generator_calculator_proto",
+    srcs = ["env_generator_calculator.proto"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/modules/face_geometry/protos:environment_proto",
+    ],
+)
+
+cc_library(
+    name = "env_generator_calculator",
+    srcs = ["env_generator_calculator.cc"],
+    deps = [
+        ":env_generator_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/modules/face_geometry/libs:validation_utils",
+        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
+    ],
+    alwayslink = 1,
+)
+
+mediapipe_proto_library(
+    name = "geometry_pipeline_calculator_proto",
+    srcs = ["geometry_pipeline_calculator.proto"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+    ],
+)
+
+cc_library(
+    name = "geometry_pipeline_calculator",
+    srcs = ["geometry_pipeline_calculator.cc"],
+    deps = [
+        ":geometry_pipeline_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/port:statusor",
+        "//mediapipe/modules/face_geometry/libs:geometry_pipeline",
+        "//mediapipe/modules/face_geometry/libs:validation_utils",
+        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_cc_proto",
+        "//mediapipe/util:resource_util",
+        "@com_google_absl//absl/memory",
+    ],
+    alwayslink = 1,
+)
diff --git a/mediapipe/modules/face_geometry/README.md b/mediapipe/modules/face_geometry/README.md
new file mode 100644
index 0000000..8427ea6
--- /dev/null
+++ b/mediapipe/modules/face_geometry/README.md
@@ -0,0 +1,20 @@
+# face_geometry
+
+Protos|Details
+:--- | :---
+[`face_geometry.Environment`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/environment.proto)| Describes an environment; includes the camera frame origin point location as well as virtual camera parameters.
+[`face_geometry.GeometryPipelineMetadata`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto)| Describes metadata needed to estimate face geometry based on the face landmark module result.
+[`face_geometry.FaceGeometry`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/face_geometry.proto)| Describes geometry data for a single face; includes a face mesh surface and a face pose in a given environment.
+[`face_geometry.Mesh3d`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/mesh_3d.proto)| Describes a 3D mesh surface.
+
+Calculators|Details
+:--- | :---
+[`FaceGeometryEnvGeneratorCalculator`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/env_generator_calculator.cc)| Generates an environment that describes a virtual scene.
+[`FaceGeometryPipelineCalculator`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc)| Extracts face geometry for multiple faces from a vector of landmark lists.
+[`FaceGeometryEffectRendererCalculator`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/effect_renderer_calculator.cc)| Renders a face effect.
+
+Subgraphs|Details
+:--- | :---
+[`FaceGeometryFromDetection`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt)| Extracts geometry from face detection for multiple faces.
+[`FaceGeometryFromLandmarks`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt)| Extracts geometry from face landmarks for multiple faces.
+[`FaceGeometry`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry.pbtxt)| Extracts geometry from face landmarks for multiple faces. Deprecated, please use `FaceGeometryFromLandmarks` in the new code.
diff --git a/mediapipe/modules/face_geometry/data/BUILD b/mediapipe/modules/face_geometry/data/BUILD
new file mode 100644
index 0000000..1661a22
--- /dev/null
+++ b/mediapipe/modules/face_geometry/data/BUILD
@@ -0,0 +1,59 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework:encode_binary_proto.bzl", "encode_binary_proto")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+encode_binary_proto(
+    name = "geometry_pipeline_metadata_detection",
+    input = "geometry_pipeline_metadata_detection.pbtxt",
+    message_type = "mediapipe.face_geometry.GeometryPipelineMetadata",
+    output = "geometry_pipeline_metadata_detection.binarypb",
+    deps = [
+        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_proto",
+    ],
+)
+
+encode_binary_proto(
+    name = "geometry_pipeline_metadata_landmarks",
+    input = "geometry_pipeline_metadata_landmarks.pbtxt",
+    message_type = "mediapipe.face_geometry.GeometryPipelineMetadata",
+    output = "geometry_pipeline_metadata_landmarks.binarypb",
+    deps = [
+        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_proto",
+    ],
+)
+
+# For backward-compatibility reasons, generate `geometry_pipeline_metadata.binarypb` from
+# the `geometry_pipeline_metadata_landmarks.pbtxt` definition.
+encode_binary_proto(
+    name = "geometry_pipeline_metadata",
+    input = "geometry_pipeline_metadata_landmarks.pbtxt",
+    message_type = "mediapipe.face_geometry.GeometryPipelineMetadata",
+    output = "geometry_pipeline_metadata.binarypb",
+    deps = [
+        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_proto",
+    ],
+)
+
+# These canonical face model files are not meant to be used in runtime, but rather for asset
+# creation and/or reference.
+exports_files([
+    "canonical_face_model.fbx",
+    "canonical_face_model.obj",
+    "canonical_face_model_uv_visualization.png",
+])
diff --git a/mediapipe/modules/face_geometry/data/canonical_face_model.fbx b/mediapipe/modules/face_geometry/data/canonical_face_model.fbx
new file mode 100644
index 0000000..8e9d24a
Binary files /dev/null and b/mediapipe/modules/face_geometry/data/canonical_face_model.fbx differ
diff --git a/mediapipe/modules/face_geometry/data/canonical_face_model.obj b/mediapipe/modules/face_geometry/data/canonical_face_model.obj
new file mode 100644
index 0000000..0e666d1
--- /dev/null
+++ b/mediapipe/modules/face_geometry/data/canonical_face_model.obj
@@ -0,0 +1,1834 @@
+v 0.000000 -3.406404 5.979507
+v 0.000000 -1.126865 7.475604
+v 0.000000 -2.089024 6.058267
+v -0.463928 0.955357 6.633583
+v 0.000000 -0.463170 7.586580
+v 0.000000 0.365669 7.242870
+v 0.000000 2.473255 5.788627
+v -4.253081 2.577646 3.279702
+v 0.000000 4.019042 5.284764
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diff --git a/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png b/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png
new file mode 100644
index 0000000..2acd991
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diff --git a/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.pbtxt b/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.pbtxt
new file mode 100644
index 0000000..c4389a6
--- /dev/null
+++ b/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.pbtxt
@@ -0,0 +1,78 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+input_source: FACE_DETECTION_PIPELINE
+procrustes_landmark_basis { landmark_id: 0 weight: 1.0 }
+procrustes_landmark_basis { landmark_id: 1 weight: 1.0 }
+procrustes_landmark_basis { landmark_id: 2 weight: 1.0 }
+procrustes_landmark_basis { landmark_id: 3 weight: 1.0 }
+procrustes_landmark_basis { landmark_id: 4 weight: 1.0 }
+procrustes_landmark_basis { landmark_id: 5 weight: 1.0 }
+# NOTE: the triangular topology of the face meshes is only useful when derived
+#       from the 468 face landmarks, not from the 6 face detection landmarks
+#       (keypoints). The former don't cover the entire face and this mesh is
+#       defined here only to comply with the API. It should be considered as
+#       a placeholder and/or for debugging purposes.
+#
+#       Use the face geometry derived from the face detection landmarks
+#       (keypoints) for the face pose transformation matrix, not the mesh.
+canonical_mesh: {
+  vertex_type: VERTEX_PT
+  primitive_type: TRIANGLE
+  vertex_buffer: -3.1511454582214355
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+  index_buffer: 0
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+  index_buffer: 3
+}
diff --git a/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.pbtxt b/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.pbtxt
new file mode 100644
index 0000000..8dfb463
--- /dev/null
+++ b/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.pbtxt
@@ -0,0 +1,5086 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+input_source: FACE_LANDMARK_PIPELINE
+procrustes_landmark_basis { landmark_id: 4 weight: 0.070909939706326 }
+procrustes_landmark_basis { landmark_id: 6 weight: 0.032100144773722 }
+procrustes_landmark_basis { landmark_id: 10 weight: 0.008446550928056 }
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+procrustes_landmark_basis { landmark_id: 54 weight: 0.007667080033571 }
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+procrustes_landmark_basis { landmark_id: 420 weight: 0.025462191551924 }
+procrustes_landmark_basis { landmark_id: 425 weight: 0.047252278774977 }
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+}
diff --git a/mediapipe/modules/face_geometry/effect_renderer_calculator.cc b/mediapipe/modules/face_geometry/effect_renderer_calculator.cc
new file mode 100644
index 0000000..f353b8f
--- /dev/null
+++ b/mediapipe/modules/face_geometry/effect_renderer_calculator.cc
@@ -0,0 +1,284 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "absl/types/optional.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/image_frame.h"
+#include "mediapipe/framework/formats/image_frame_opencv.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"       // NOTYPO
+#include "mediapipe/framework/port/opencv_imgcodecs_inc.h"  // NOTYPO
+#include "mediapipe/framework/port/opencv_imgproc_inc.h"    // NOTYPO
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_macros.h"
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/gpu/gl_calculator_helper.h"
+#include "mediapipe/gpu/gpu_buffer.h"
+#include "mediapipe/modules/face_geometry/effect_renderer_calculator.pb.h"
+#include "mediapipe/modules/face_geometry/libs/effect_renderer.h"
+#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+#include "mediapipe/util/resource_util.h"
+
+namespace mediapipe {
+namespace {
+
+static constexpr char kEnvironmentTag[] = "ENVIRONMENT";
+static constexpr char kImageGpuTag[] = "IMAGE_GPU";
+static constexpr char kMultiFaceGeometryTag[] = "MULTI_FACE_GEOMETRY";
+
+// A calculator that renders a visual effect for multiple faces.
+//
+// Inputs:
+//   IMAGE_GPU (`GpuBuffer`, required):
+//     A buffer containing input image.
+//
+//   MULTI_FACE_GEOMETRY (`std::vector<face_geometry::FaceGeometry>`, optional):
+//     A vector of face geometry data.
+//
+//     If absent, the input GPU buffer is copied over into the output GPU buffer
+//     without any effect being rendered.
+//
+// Input side packets:
+//   ENVIRONMENT (`face_geometry::Environment`, required)
+//     Describes an environment; includes the camera frame origin point location
+//     as well as virtual camera parameters.
+//
+// Output:
+//   IMAGE_GPU (`GpuBuffer`, required):
+//     A buffer with a visual effect being rendered for multiple faces.
+//
+// Options:
+//   effect_texture_path (`string`, required):
+//     Defines a path for the visual effect texture file. The effect texture is
+//     later rendered on top of the effect mesh.
+//
+//     The texture file format must be supported by the OpenCV image decoder. It
+//     must also define either an RGB or an RGBA texture.
+//
+//   effect_mesh_3d_path (`string`, optional):
+//     Defines a path for the visual effect mesh 3D file. The effect mesh is
+//     later "attached" to the face and is driven by the face pose
+//     transformation matrix.
+//
+//     The mesh 3D file format must be the binary `face_geometry.Mesh3d` proto.
+//
+//     If is not present, the runtime face mesh will be used as the effect mesh
+//     - this mode is handy for facepaint effects.
+//
+class EffectRendererCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    MP_RETURN_IF_ERROR(mediapipe::GlCalculatorHelper::UpdateContract(cc))
+        << "Failed to update contract for the GPU helper!";
+
+    cc->InputSidePackets()
+        .Tag(kEnvironmentTag)
+        .Set<face_geometry::Environment>();
+    cc->Inputs().Tag(kImageGpuTag).Set<GpuBuffer>();
+    cc->Inputs()
+        .Tag(kMultiFaceGeometryTag)
+        .Set<std::vector<face_geometry::FaceGeometry>>();
+    cc->Outputs().Tag(kImageGpuTag).Set<GpuBuffer>();
+
+    return mediapipe::GlCalculatorHelper::UpdateContract(cc);
+  }
+
+  absl::Status Open(CalculatorContext* cc) override {
+    cc->SetOffset(mediapipe::TimestampDiff(0));
+
+    MP_RETURN_IF_ERROR(gpu_helper_.Open(cc))
+        << "Failed to open the GPU helper!";
+    return gpu_helper_.RunInGlContext([&]() -> absl::Status {
+      const auto& options =
+          cc->Options<FaceGeometryEffectRendererCalculatorOptions>();
+
+      const auto& environment = cc->InputSidePackets()
+                                    .Tag(kEnvironmentTag)
+                                    .Get<face_geometry::Environment>();
+
+      MP_RETURN_IF_ERROR(face_geometry::ValidateEnvironment(environment))
+          << "Invalid environment!";
+
+      absl::optional<face_geometry::Mesh3d> effect_mesh_3d;
+      if (options.has_effect_mesh_3d_path()) {
+        ASSIGN_OR_RETURN(effect_mesh_3d,
+                         ReadMesh3dFromFile(options.effect_mesh_3d_path()),
+                         _ << "Failed to read the effect 3D mesh from file!");
+
+        MP_RETURN_IF_ERROR(face_geometry::ValidateMesh3d(*effect_mesh_3d))
+            << "Invalid effect 3D mesh!";
+      }
+
+      ASSIGN_OR_RETURN(ImageFrame effect_texture,
+                       ReadTextureFromFile(options.effect_texture_path()),
+                       _ << "Failed to read the effect texture from file!");
+
+      ASSIGN_OR_RETURN(effect_renderer_,
+                       CreateEffectRenderer(environment, effect_mesh_3d,
+                                            std::move(effect_texture)),
+                       _ << "Failed to create the effect renderer!");
+
+      return absl::OkStatus();
+    });
+  }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    // The `IMAGE_GPU` stream is required to have a non-empty packet. In case
+    // this requirement is not met, there's nothing to be processed at the
+    // current timestamp.
+    if (cc->Inputs().Tag(kImageGpuTag).IsEmpty()) {
+      return absl::OkStatus();
+    }
+
+    return gpu_helper_.RunInGlContext([this, cc]() -> absl::Status {
+      const auto& input_gpu_buffer =
+          cc->Inputs().Tag(kImageGpuTag).Get<GpuBuffer>();
+
+      GlTexture input_gl_texture =
+          gpu_helper_.CreateSourceTexture(input_gpu_buffer);
+
+      GlTexture output_gl_texture = gpu_helper_.CreateDestinationTexture(
+          input_gl_texture.width(), input_gl_texture.height());
+
+      std::vector<face_geometry::FaceGeometry> empty_multi_face_geometry;
+      const auto& multi_face_geometry =
+          cc->Inputs().Tag(kMultiFaceGeometryTag).IsEmpty()
+              ? empty_multi_face_geometry
+              : cc->Inputs()
+                    .Tag(kMultiFaceGeometryTag)
+                    .Get<std::vector<face_geometry::FaceGeometry>>();
+
+      // Validate input multi face geometry data.
+      for (const face_geometry::FaceGeometry& face_geometry :
+           multi_face_geometry) {
+        MP_RETURN_IF_ERROR(face_geometry::ValidateFaceGeometry(face_geometry))
+            << "Invalid face geometry!";
+      }
+
+      MP_RETURN_IF_ERROR(effect_renderer_->RenderEffect(
+          multi_face_geometry, input_gl_texture.width(),
+          input_gl_texture.height(), input_gl_texture.target(),
+          input_gl_texture.name(), output_gl_texture.target(),
+          output_gl_texture.name()))
+          << "Failed to render the effect!";
+
+      std::unique_ptr<GpuBuffer> output_gpu_buffer =
+          output_gl_texture.GetFrame<GpuBuffer>();
+
+      cc->Outputs()
+          .Tag(kImageGpuTag)
+          .AddPacket(mediapipe::Adopt<GpuBuffer>(output_gpu_buffer.release())
+                         .At(cc->InputTimestamp()));
+
+      output_gl_texture.Release();
+      input_gl_texture.Release();
+
+      return absl::OkStatus();
+    });
+  }
+
+  ~EffectRendererCalculator() {
+    gpu_helper_.RunInGlContext([this]() { effect_renderer_.reset(); });
+  }
+
+ private:
+  static absl::StatusOr<ImageFrame> ReadTextureFromFile(
+      const std::string& texture_path) {
+    ASSIGN_OR_RETURN(std::string texture_blob,
+                     ReadContentBlobFromFile(texture_path),
+                     _ << "Failed to read texture blob from file!");
+
+    // Use OpenCV image decoding functionality to finish reading the texture.
+    std::vector<char> texture_blob_vector(texture_blob.begin(),
+                                          texture_blob.end());
+    cv::Mat decoded_mat =
+        cv::imdecode(texture_blob_vector, cv::IMREAD_UNCHANGED);
+
+    RET_CHECK(decoded_mat.type() == CV_8UC3 || decoded_mat.type() == CV_8UC4)
+        << "Texture must have `char` as the underlying type and "
+           "must have either 3 or 4 channels!";
+
+    ImageFormat::Format image_format = ImageFormat::UNKNOWN;
+    cv::Mat output_mat;
+    switch (decoded_mat.channels()) {
+      case 3:
+        image_format = ImageFormat::SRGB;
+        cv::cvtColor(decoded_mat, output_mat, cv::COLOR_BGR2RGB);
+        break;
+
+      case 4:
+        image_format = ImageFormat::SRGBA;
+        cv::cvtColor(decoded_mat, output_mat, cv::COLOR_BGRA2RGBA);
+        break;
+
+      default:
+        RET_CHECK_FAIL()
+            << "Unexpected number of channels; expected 3 or 4, got "
+            << decoded_mat.channels() << "!";
+    }
+
+    ImageFrame output_image_frame(image_format, output_mat.size().width,
+                                  output_mat.size().height,
+                                  ImageFrame::kGlDefaultAlignmentBoundary);
+
+    output_mat.copyTo(formats::MatView(&output_image_frame));
+
+    return output_image_frame;
+  }
+
+  static absl::StatusOr<face_geometry::Mesh3d> ReadMesh3dFromFile(
+      const std::string& mesh_3d_path) {
+    ASSIGN_OR_RETURN(std::string mesh_3d_blob,
+                     ReadContentBlobFromFile(mesh_3d_path),
+                     _ << "Failed to read mesh 3D blob from file!");
+
+    face_geometry::Mesh3d mesh_3d;
+    RET_CHECK(mesh_3d.ParseFromString(mesh_3d_blob))
+        << "Failed to parse a mesh 3D proto from a binary blob!";
+
+    return mesh_3d;
+  }
+
+  static absl::StatusOr<std::string> ReadContentBlobFromFile(
+      const std::string& unresolved_path) {
+    ASSIGN_OR_RETURN(std::string resolved_path,
+                     mediapipe::PathToResourceAsFile(unresolved_path),
+                     _ << "Failed to resolve path! Path = " << unresolved_path);
+
+    std::string content_blob;
+    MP_RETURN_IF_ERROR(
+        mediapipe::GetResourceContents(resolved_path, &content_blob))
+        << "Failed to read content blob! Resolved path = " << resolved_path;
+
+    return content_blob;
+  }
+
+  mediapipe::GlCalculatorHelper gpu_helper_;
+  std::unique_ptr<face_geometry::EffectRenderer> effect_renderer_;
+};
+
+}  // namespace
+
+using FaceGeometryEffectRendererCalculator = EffectRendererCalculator;
+
+REGISTER_CALCULATOR(FaceGeometryEffectRendererCalculator);
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/effect_renderer_calculator.proto b/mediapipe/modules/face_geometry/effect_renderer_calculator.proto
new file mode 100644
index 0000000..6c23903
--- /dev/null
+++ b/mediapipe/modules/face_geometry/effect_renderer_calculator.proto
@@ -0,0 +1,46 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator_options.proto";
+
+message FaceGeometryEffectRendererCalculatorOptions {
+  extend CalculatorOptions {
+    optional FaceGeometryEffectRendererCalculatorOptions ext = 323693808;
+  }
+
+  // Defines a path for the visual effect texture file. The effect texture is
+  // later rendered on top of the effect mesh.
+  //
+  // Please be aware about the difference between the CPU texture memory layout
+  // and the GPU texture sampler coordinate space. This renderer follows
+  // conventions discussed here: https://open.gl/textures
+  //
+  // The texture file format must be supported by the OpenCV image decoder. It
+  // must also define either an RGB or an RGBA texture.
+  optional string effect_texture_path = 1;
+
+  // Defines a path for the visual effect mesh 3D file. The effect mesh is later
+  // "attached" to the face and is driven by the face pose transformation
+  // matrix.
+  //
+  // The mesh 3D file format must be the binary `face_system.Mesh3d` proto.
+  //
+  // If is not present, the runtime face mesh will be used as the effect mesh
+  // - this mode is handy for facepaint effects.
+  optional string effect_mesh_3d_path = 2;
+}
diff --git a/mediapipe/modules/face_geometry/env_generator_calculator.cc b/mediapipe/modules/face_geometry/env_generator_calculator.cc
new file mode 100644
index 0000000..2e95a66
--- /dev/null
+++ b/mediapipe/modules/face_geometry/env_generator_calculator.cc
@@ -0,0 +1,81 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_macros.h"
+#include "mediapipe/modules/face_geometry/env_generator_calculator.pb.h"
+#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+
+namespace mediapipe {
+namespace {
+
+static constexpr char kEnvironmentTag[] = "ENVIRONMENT";
+
+// A calculator that generates an environment, which describes a virtual scene.
+//
+// Output side packets:
+//   ENVIRONMENT (`face_geometry::Environment`, required)
+//     Describes an environment; includes the camera frame origin point location
+//     as well as virtual camera parameters.
+//
+// Options:
+//   environment (`face_geometry.Environment`, required):
+//     Defines an environment to be packed as the output side packet.
+//
+//     Must be valid (for details, please refer to the proto message definition
+//     comments and/or `modules/face_geometry/libs/validation_utils.h/cc`)
+//
+class EnvGeneratorCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->OutputSidePackets()
+        .Tag(kEnvironmentTag)
+        .Set<face_geometry::Environment>();
+    return absl::OkStatus();
+  }
+
+  absl::Status Open(CalculatorContext* cc) override {
+    cc->SetOffset(mediapipe::TimestampDiff(0));
+
+    const face_geometry::Environment& environment =
+        cc->Options<FaceGeometryEnvGeneratorCalculatorOptions>().environment();
+
+    MP_RETURN_IF_ERROR(face_geometry::ValidateEnvironment(environment))
+        << "Invalid environment!";
+
+    cc->OutputSidePackets()
+        .Tag(kEnvironmentTag)
+        .Set(mediapipe::MakePacket<face_geometry::Environment>(environment));
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    return absl::OkStatus();
+  }
+
+  absl::Status Close(CalculatorContext* cc) override {
+    return absl::OkStatus();
+  }
+};
+
+}  // namespace
+
+using FaceGeometryEnvGeneratorCalculator = EnvGeneratorCalculator;
+
+REGISTER_CALCULATOR(FaceGeometryEnvGeneratorCalculator);
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/env_generator_calculator.proto b/mediapipe/modules/face_geometry/env_generator_calculator.proto
new file mode 100644
index 0000000..dea2ae0
--- /dev/null
+++ b/mediapipe/modules/face_geometry/env_generator_calculator.proto
@@ -0,0 +1,32 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator_options.proto";
+import "mediapipe/modules/face_geometry/protos/environment.proto";
+
+message FaceGeometryEnvGeneratorCalculatorOptions {
+  extend CalculatorOptions {
+    optional FaceGeometryEnvGeneratorCalculatorOptions ext = 323693810;
+  }
+
+  // Defines an environment to be packed as the output side packet.
+  //
+  // Must be valid (for details, please refer to the proto message definition
+  // comments and/or `modules/face_geometry/libs/validation_utils.h/cc`)
+  optional face_geometry.Environment environment = 1;
+}
diff --git a/mediapipe/modules/face_geometry/face_geometry.pbtxt b/mediapipe/modules/face_geometry/face_geometry.pbtxt
new file mode 100644
index 0000000..76228d4
--- /dev/null
+++ b/mediapipe/modules/face_geometry/face_geometry.pbtxt
@@ -0,0 +1,48 @@
+# MediaPipe graph to extract geometry from face landmarks for multiple faces.
+#
+# It is required that "geometry_pipeline_metadata.binarypb" is available at
+# "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata.binarypb"
+# path during execution.
+#
+# This is a deprecated subgraph kept for backward-compatibility reasons. Please,
+# be explicit and use the `FaceGeometryFromLandmarks` subgraph in the new code
+# to enable the same runtime behaviour.
+
+type: "FaceGeometry"
+
+# The size of the input frame. The first element of the pair is the frame width;
+# the other one is the frame height.
+#
+# The face landmarks should have been detected on a frame with the same
+# ratio. If used as-is, the resulting face geometry visualization should be
+# happening on a frame with the same ratio as well.
+#
+# (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# Collection of detected/predicted faces, each represented as a list of face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
+
+# Environment that describes the current virtual scene.
+# (face_geometry::Environment)
+input_side_packet: "ENVIRONMENT:environment"
+
+# A list of geometry data for each detected face.
+# (std::vector<face_geometry::FaceGeometry>)
+output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+
+# Extracts face geometry for multiple faces from a vector of face landmark
+# lists.
+node {
+  calculator: "FaceGeometryPipelineCalculator"
+  input_side_packet: "ENVIRONMENT:environment"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
+  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+  options: {
+    [mediapipe.FaceGeometryPipelineCalculatorOptions.ext] {
+      metadata_path: "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata.binarypb"
+    }
+  }
+}
diff --git a/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt b/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt
new file mode 100644
index 0000000..f570286
--- /dev/null
+++ b/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt
@@ -0,0 +1,87 @@
+# MediaPipe graph to extract geometry from face detection for multiple faces.
+#
+# It is required that "geometry_pipeline_metadata_detection.binarypb" is
+# available at
+# "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.binarypb"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceGeometryFromDetection"
+#     input_stream: "IMAGE_SIZE:image_size"
+#     input_stream: "MULTI_FACE_DETECTION:multi_face_detection"
+#     input_side_packet: "ENVIRONMENT:environment"
+#     output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+#   }
+
+type: "FaceGeometryFromDetection"
+
+# The size of the input frame. The first element of the pair is the frame width;
+# the other one is the frame height.
+#
+# The face landmarks should have been detected on a frame with the same
+# ratio. If used as-is, the resulting face geometry visualization should be
+# happening on a frame with the same ratio as well.
+#
+# (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# Collection of detected/predicted faces, each represented as a detection.
+# (std::vector<DETECTION>)
+input_stream: "MULTI_FACE_DETECTION:multi_face_detection"
+
+# Environment that describes the current virtual scene.
+# (face_geometry::Environment)
+input_side_packet: "ENVIRONMENT:environment"
+
+# A list of geometry data for each detected face.
+# (std::vector<face_geometry::FaceGeometry>)
+#
+# NOTE: the triangular topology of the face meshes is only useful when derived
+#       from the 468 face landmarks, not from the 6 face detection landmarks
+#       (keypoints). The former don't cover the entire face and this mesh is
+#       defined here only to comply with the API. It should be considered as
+#       a placeholder and/or for debugging purposes.
+#
+#       Use the face geometry derived from the face detection landmarks
+#       (keypoints) for the face pose transformation matrix, not the mesh.
+output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+
+# Begin iterating over a vector of the face detections.
+node {
+  calculator: "BeginLoopDetectionCalculator"
+  input_stream: "ITERABLE:multi_face_detection"
+  output_stream: "ITEM:face_detection"
+  output_stream: "BATCH_END:detection_timestamp"
+}
+
+# Extracts face detection keypoints as a normalized landmarks.
+node {
+  calculator: "DetectionToLandmarksCalculator"
+  input_stream: "DETECTION:face_detection"
+  output_stream: "LANDMARKS:face_landmarks"
+}
+
+# End iterating over a vector of the face detections and receive a vector of
+# face landmark lists as a result.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:face_landmarks"
+  input_stream: "BATCH_END:detection_timestamp"
+  output_stream: "ITERABLE:multi_face_landmarks"
+}
+
+# Extracts face geometry for multiple faces from a vector of face detection
+# landmark lists.
+node {
+  calculator: "FaceGeometryPipelineCalculator"
+  input_side_packet: "ENVIRONMENT:environment"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
+  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+  options: {
+    [mediapipe.FaceGeometryPipelineCalculatorOptions.ext] {
+      metadata_path: "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.binarypb"
+    }
+  }
+}
diff --git a/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt b/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt
new file mode 100644
index 0000000..3291476
--- /dev/null
+++ b/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt
@@ -0,0 +1,54 @@
+# MediaPipe graph to extract geometry from face landmarks for multiple faces.
+#
+# It is required that "geometry_pipeline_metadata_from_landmark.binarypb" is
+# available at
+# "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_from_landmarks.binarypb"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceGeometryFromLandmarks"
+#     input_stream: "IMAGE_SIZE:image_size"
+#     input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
+#     input_side_packet: "ENVIRONMENT:environment"
+#     output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+#   }
+
+type: "FaceGeometryFromLandmarks"
+
+# The size of the input frame. The first element of the pair is the frame width;
+# the other one is the frame height.
+#
+# The face landmarks should have been detected on a frame with the same
+# ratio. If used as-is, the resulting face geometry visualization should be
+# happening on a frame with the same ratio as well.
+#
+# (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# Collection of detected/predicted faces, each represented as a list of face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
+
+# Environment that describes the current virtual scene.
+# (face_geometry::Environment)
+input_side_packet: "ENVIRONMENT:environment"
+
+# A list of geometry data for each detected face.
+# (std::vector<face_geometry::FaceGeometry>)
+output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+
+# Extracts face geometry for multiple faces from a vector of face landmark
+# lists.
+node {
+  calculator: "FaceGeometryPipelineCalculator"
+  input_side_packet: "ENVIRONMENT:environment"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
+  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
+  options: {
+    [mediapipe.FaceGeometryPipelineCalculatorOptions.ext] {
+      metadata_path: "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.binarypb"
+    }
+  }
+}
diff --git a/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc b/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc
new file mode 100644
index 0000000..87e710e
--- /dev/null
+++ b/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc
@@ -0,0 +1,197 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "absl/memory/memory.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_macros.h"
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/modules/face_geometry/geometry_pipeline_calculator.pb.h"
+#include "mediapipe/modules/face_geometry/libs/geometry_pipeline.h"
+#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
+#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
+#include "mediapipe/util/resource_util.h"
+
+namespace mediapipe {
+namespace {
+
+static constexpr char kEnvironmentTag[] = "ENVIRONMENT";
+static constexpr char kImageSizeTag[] = "IMAGE_SIZE";
+static constexpr char kMultiFaceGeometryTag[] = "MULTI_FACE_GEOMETRY";
+static constexpr char kMultiFaceLandmarksTag[] = "MULTI_FACE_LANDMARKS";
+
+// A calculator that renders a visual effect for multiple faces.
+//
+// Inputs:
+//   IMAGE_SIZE (`std::pair<int, int>`, required):
+//     The size of the current frame. The first element of the pair is the frame
+//     width; the other one is the frame height.
+//
+//     The face landmarks should have been detected on a frame with the same
+//     ratio. If used as-is, the resulting face geometry visualization should be
+//     happening on a frame with the same ratio as well.
+//
+//   MULTI_FACE_LANDMARKS (`std::vector<NormalizedLandmarkList>`, required):
+//     A vector of face landmark lists.
+//
+// Input side packets:
+//   ENVIRONMENT (`face_geometry::Environment`, required)
+//     Describes an environment; includes the camera frame origin point location
+//     as well as virtual camera parameters.
+//
+// Output:
+//   MULTI_FACE_GEOMETRY (`std::vector<face_geometry::FaceGeometry>`, required):
+//     A vector of face geometry data.
+//
+// Options:
+//   metadata_path (`string`, optional):
+//     Defines a path for the geometry pipeline metadata file.
+//
+//     The geometry pipeline metadata file format must be the binary
+//     `face_geometry.GeometryPipelineMetadata` proto.
+//
+class GeometryPipelineCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->InputSidePackets()
+        .Tag(kEnvironmentTag)
+        .Set<face_geometry::Environment>();
+    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
+    cc->Inputs()
+        .Tag(kMultiFaceLandmarksTag)
+        .Set<std::vector<NormalizedLandmarkList>>();
+    cc->Outputs()
+        .Tag(kMultiFaceGeometryTag)
+        .Set<std::vector<face_geometry::FaceGeometry>>();
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Open(CalculatorContext* cc) override {
+    cc->SetOffset(mediapipe::TimestampDiff(0));
+
+    const auto& options = cc->Options<FaceGeometryPipelineCalculatorOptions>();
+
+    ASSIGN_OR_RETURN(
+        face_geometry::GeometryPipelineMetadata metadata,
+        ReadMetadataFromFile(options.metadata_path()),
+        _ << "Failed to read the geometry pipeline metadata from file!");
+
+    MP_RETURN_IF_ERROR(
+        face_geometry::ValidateGeometryPipelineMetadata(metadata))
+        << "Invalid geometry pipeline metadata!";
+
+    const face_geometry::Environment& environment =
+        cc->InputSidePackets()
+            .Tag(kEnvironmentTag)
+            .Get<face_geometry::Environment>();
+
+    MP_RETURN_IF_ERROR(face_geometry::ValidateEnvironment(environment))
+        << "Invalid environment!";
+
+    ASSIGN_OR_RETURN(
+        geometry_pipeline_,
+        face_geometry::CreateGeometryPipeline(environment, metadata),
+        _ << "Failed to create a geometry pipeline!");
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    // Both the `IMAGE_SIZE` and the `MULTI_FACE_LANDMARKS` streams are required
+    // to have a non-empty packet. In case this requirement is not met, there's
+    // nothing to be processed at the current timestamp.
+    if (cc->Inputs().Tag(kImageSizeTag).IsEmpty() ||
+        cc->Inputs().Tag(kMultiFaceLandmarksTag).IsEmpty()) {
+      return absl::OkStatus();
+    }
+
+    const auto& image_size =
+        cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
+    const auto& multi_face_landmarks =
+        cc->Inputs()
+            .Tag(kMultiFaceLandmarksTag)
+            .Get<std::vector<NormalizedLandmarkList>>();
+
+    auto multi_face_geometry =
+        absl::make_unique<std::vector<face_geometry::FaceGeometry>>();
+
+    ASSIGN_OR_RETURN(
+        *multi_face_geometry,
+        geometry_pipeline_->EstimateFaceGeometry(
+            multi_face_landmarks,  //
+            /*frame_width*/ image_size.first,
+            /*frame_height*/ image_size.second),
+        _ << "Failed to estimate face geometry for multiple faces!");
+
+    cc->Outputs()
+        .Tag(kMultiFaceGeometryTag)
+        .AddPacket(mediapipe::Adopt<std::vector<face_geometry::FaceGeometry>>(
+                       multi_face_geometry.release())
+                       .At(cc->InputTimestamp()));
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Close(CalculatorContext* cc) override {
+    return absl::OkStatus();
+  }
+
+ private:
+  static absl::StatusOr<face_geometry::GeometryPipelineMetadata>
+  ReadMetadataFromFile(const std::string& metadata_path) {
+    ASSIGN_OR_RETURN(std::string metadata_blob,
+                     ReadContentBlobFromFile(metadata_path),
+                     _ << "Failed to read a metadata blob from file!");
+
+    face_geometry::GeometryPipelineMetadata metadata;
+    RET_CHECK(metadata.ParseFromString(metadata_blob))
+        << "Failed to parse a metadata proto from a binary blob!";
+
+    return metadata;
+  }
+
+  static absl::StatusOr<std::string> ReadContentBlobFromFile(
+      const std::string& unresolved_path) {
+    ASSIGN_OR_RETURN(std::string resolved_path,
+                     mediapipe::PathToResourceAsFile(unresolved_path),
+                     _ << "Failed to resolve path! Path = " << unresolved_path);
+
+    std::string content_blob;
+    MP_RETURN_IF_ERROR(
+        mediapipe::GetResourceContents(resolved_path, &content_blob))
+        << "Failed to read content blob! Resolved path = " << resolved_path;
+
+    return content_blob;
+  }
+
+  std::unique_ptr<face_geometry::GeometryPipeline> geometry_pipeline_;
+};
+
+}  // namespace
+
+using FaceGeometryPipelineCalculator = GeometryPipelineCalculator;
+
+REGISTER_CALCULATOR(FaceGeometryPipelineCalculator);
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/geometry_pipeline_calculator.proto b/mediapipe/modules/face_geometry/geometry_pipeline_calculator.proto
new file mode 100644
index 0000000..638bb45
--- /dev/null
+++ b/mediapipe/modules/face_geometry/geometry_pipeline_calculator.proto
@@ -0,0 +1,27 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator_options.proto";
+
+message FaceGeometryPipelineCalculatorOptions {
+  extend CalculatorOptions {
+    optional FaceGeometryPipelineCalculatorOptions ext = 323693812;
+  }
+
+  optional string metadata_path = 1;
+}
diff --git a/mediapipe/modules/face_geometry/libs/BUILD b/mediapipe/modules/face_geometry/libs/BUILD
new file mode 100644
index 0000000..35dc451
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/BUILD
@@ -0,0 +1,103 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "effect_renderer",
+    srcs = ["effect_renderer.cc"],
+    hdrs = ["effect_renderer.h"],
+    deps = [
+        ":mesh_3d_utils",
+        ":validation_utils",
+        "//mediapipe/framework/formats:image_format_cc_proto",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/formats:matrix_data_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/port:statusor",
+        "//mediapipe/gpu:gl_base",
+        "//mediapipe/gpu:shader_util",
+        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/types:optional",
+    ],
+)
+
+cc_library(
+    name = "geometry_pipeline",
+    srcs = ["geometry_pipeline.cc"],
+    hdrs = ["geometry_pipeline.h"],
+    deps = [
+        ":mesh_3d_utils",
+        ":procrustes_solver",
+        ":validation_utils",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/formats:matrix",
+        "//mediapipe/framework/formats:matrix_data_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/port:statusor",
+        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
+        "@com_google_absl//absl/memory",
+        "@eigen_archive//:eigen3",
+    ],
+)
+
+cc_library(
+    name = "mesh_3d_utils",
+    srcs = ["mesh_3d_utils.cc"],
+    hdrs = ["mesh_3d_utils.h"],
+    deps = [
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:statusor",
+        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
+    ],
+)
+
+cc_library(
+    name = "procrustes_solver",
+    srcs = ["procrustes_solver.cc"],
+    hdrs = ["procrustes_solver.h"],
+    deps = [
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/port:statusor",
+        "@com_google_absl//absl/memory",
+        "@eigen_archive//:eigen3",
+    ],
+)
+
+cc_library(
+    name = "validation_utils",
+    srcs = ["validation_utils.cc"],
+    hdrs = ["validation_utils.h"],
+    deps = [
+        ":mesh_3d_utils",
+        "//mediapipe/framework/formats:matrix_data_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_cc_proto",
+        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
+    ],
+)
diff --git a/mediapipe/modules/face_geometry/libs/effect_renderer.cc b/mediapipe/modules/face_geometry/libs/effect_renderer.cc
new file mode 100644
index 0000000..27a54e0
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/effect_renderer.cc
@@ -0,0 +1,733 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/face_geometry/libs/effect_renderer.h"
+
+#include <cmath>
+#include <cstdint>
+#include <limits>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "absl/memory/memory.h"
+#include "absl/types/optional.h"
+#include "mediapipe/framework/formats/image_format.pb.h"
+#include "mediapipe/framework/formats/image_frame.h"
+#include "mediapipe/framework/formats/matrix_data.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_macros.h"
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/gpu/gl_base.h"
+#include "mediapipe/gpu/shader_util.h"
+#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
+#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+
+namespace mediapipe::face_geometry {
+namespace {
+
+struct RenderableMesh3d {
+  static absl::StatusOr<RenderableMesh3d> CreateFromProtoMesh3d(
+      const Mesh3d& proto_mesh_3d) {
+    Mesh3d::VertexType vertex_type = proto_mesh_3d.vertex_type();
+
+    RenderableMesh3d renderable_mesh_3d;
+    renderable_mesh_3d.vertex_size = GetVertexSize(vertex_type);
+    ASSIGN_OR_RETURN(
+        renderable_mesh_3d.vertex_position_size,
+        GetVertexComponentSize(vertex_type, VertexComponent::POSITION),
+        _ << "Failed to get the position vertex size!");
+    ASSIGN_OR_RETURN(
+        renderable_mesh_3d.tex_coord_position_size,
+        GetVertexComponentSize(vertex_type, VertexComponent::TEX_COORD),
+        _ << "Failed to get the tex coord vertex size!");
+    ASSIGN_OR_RETURN(
+        renderable_mesh_3d.vertex_position_offset,
+        GetVertexComponentOffset(vertex_type, VertexComponent::POSITION),
+        _ << "Failed to get the position vertex offset!");
+    ASSIGN_OR_RETURN(
+        renderable_mesh_3d.tex_coord_position_offset,
+        GetVertexComponentOffset(vertex_type, VertexComponent::TEX_COORD),
+        _ << "Failed to get the tex coord vertex offset!");
+
+    switch (proto_mesh_3d.primitive_type()) {
+      case Mesh3d::TRIANGLE:
+        renderable_mesh_3d.primitive_type = GL_TRIANGLES;
+        break;
+
+      default:
+        RET_CHECK_FAIL() << "Only triangle primitive types are supported!";
+    }
+
+    renderable_mesh_3d.vertex_buffer.reserve(
+        proto_mesh_3d.vertex_buffer_size());
+    for (float vertex_element : proto_mesh_3d.vertex_buffer()) {
+      renderable_mesh_3d.vertex_buffer.push_back(vertex_element);
+    }
+
+    renderable_mesh_3d.index_buffer.reserve(proto_mesh_3d.index_buffer_size());
+    for (uint32_t index_element : proto_mesh_3d.index_buffer()) {
+      RET_CHECK_LE(index_element, std::numeric_limits<uint16_t>::max())
+          << "Index buffer elements must fit into the `uint16` type in order "
+             "to be renderable!";
+
+      renderable_mesh_3d.index_buffer.push_back(
+          static_cast<uint16_t>(index_element));
+    }
+
+    return renderable_mesh_3d;
+  }
+
+  uint32_t vertex_size;
+  uint32_t vertex_position_size;
+  uint32_t tex_coord_position_size;
+  uint32_t vertex_position_offset;
+  uint32_t tex_coord_position_offset;
+  uint32_t primitive_type;
+
+  std::vector<float> vertex_buffer;
+  std::vector<uint16_t> index_buffer;
+};
+
+class Texture {
+ public:
+  static absl::StatusOr<std::unique_ptr<Texture>> WrapExternalTexture(
+      GLuint handle, GLenum target, int width, int height) {
+    RET_CHECK(handle) << "External texture must have a non-null handle!";
+    return absl::WrapUnique(new Texture(handle, target, width, height,
+                                        /*is_owned*/ false));
+  }
+
+  static absl::StatusOr<std::unique_ptr<Texture>> CreateFromImageFrame(
+      const ImageFrame& image_frame) {
+    RET_CHECK(image_frame.IsAligned(ImageFrame::kGlDefaultAlignmentBoundary))
+        << "Image frame memory must be aligned for GL usage!";
+
+    RET_CHECK(image_frame.Width() > 0 && image_frame.Height() > 0)
+        << "Image frame must have positive dimensions!";
+
+    RET_CHECK(image_frame.Format() == ImageFormat::SRGB ||
+              image_frame.Format() == ImageFormat::SRGBA)
+        << "Image frame format must be either SRGB or SRGBA!";
+
+    GLint image_format;
+    switch (image_frame.NumberOfChannels()) {
+      case 3:
+        image_format = GL_RGB;
+        break;
+      case 4:
+        image_format = GL_RGBA;
+        break;
+      default:
+        RET_CHECK_FAIL()
+            << "Unexpected number of channels; expected 3 or 4, got "
+            << image_frame.NumberOfChannels() << "!";
+    }
+
+    GLuint handle;
+    glGenTextures(1, &handle);
+    RET_CHECK(handle) << "Failed to initialize an OpenGL texture!";
+
+    glBindTexture(GL_TEXTURE_2D, handle);
+    glTexParameteri(GL_TEXTURE_2D, GL_NEAREST_MIPMAP_LINEAR, GL_LINEAR);
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR);
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE);
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE);
+    glTexImage2D(GL_TEXTURE_2D, 0, image_format, image_frame.Width(),
+                 image_frame.Height(), 0, image_format, GL_UNSIGNED_BYTE,
+                 image_frame.PixelData());
+    glGenerateMipmap(GL_TEXTURE_2D);
+    glBindTexture(GL_TEXTURE_2D, 0);
+
+    return absl::WrapUnique(new Texture(
+        handle, GL_TEXTURE_2D, image_frame.Width(), image_frame.Height(),
+        /*is_owned*/ true));
+  }
+
+  ~Texture() {
+    if (is_owned_) {
+      glDeleteProgram(handle_);
+    }
+  }
+
+  GLuint handle() const { return handle_; }
+  GLenum target() const { return target_; }
+  int width() const { return width_; }
+  int height() const { return height_; }
+
+ private:
+  Texture(GLuint handle, GLenum target, int width, int height, bool is_owned)
+      : handle_(handle),
+        target_(target),
+        width_(width),
+        height_(height),
+        is_owned_(is_owned) {}
+
+  GLuint handle_;
+  GLenum target_;
+  int width_;
+  int height_;
+  bool is_owned_;
+};
+
+class RenderTarget {
+ public:
+  static absl::StatusOr<std::unique_ptr<RenderTarget>> Create() {
+    GLuint framebuffer_handle;
+    glGenFramebuffers(1, &framebuffer_handle);
+    RET_CHECK(framebuffer_handle)
+        << "Failed to initialize an OpenGL framebuffer!";
+
+    return absl::WrapUnique(new RenderTarget(framebuffer_handle));
+  }
+
+  ~RenderTarget() {
+    glDeleteFramebuffers(1, &framebuffer_handle_);
+    // Renderbuffer handle might have never been created if this render target
+    // is destroyed before `SetColorbuffer()` is called for the first time.
+    if (renderbuffer_handle_) {
+      glDeleteFramebuffers(1, &renderbuffer_handle_);
+    }
+  }
+
+  absl::Status SetColorbuffer(const Texture& colorbuffer_texture) {
+    glBindFramebuffer(GL_FRAMEBUFFER, framebuffer_handle_);
+    glViewport(0, 0, colorbuffer_texture.width(), colorbuffer_texture.height());
+
+    glActiveTexture(GL_TEXTURE0);
+    glBindTexture(colorbuffer_texture.target(), colorbuffer_texture.handle());
+    glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0,
+                           colorbuffer_texture.target(),
+                           colorbuffer_texture.handle(),
+                           /*level*/ 0);
+    glBindTexture(colorbuffer_texture.target(), 0);
+
+    // If the existing depth buffer has different dimensions, delete it.
+    if (renderbuffer_handle_ &&
+        (viewport_width_ != colorbuffer_texture.width() ||
+         viewport_height_ != colorbuffer_texture.height())) {
+      glDeleteRenderbuffers(1, &renderbuffer_handle_);
+      renderbuffer_handle_ = 0;
+    }
+
+    // If there is no depth buffer, create one.
+    if (!renderbuffer_handle_) {
+      glGenRenderbuffers(1, &renderbuffer_handle_);
+      RET_CHECK(renderbuffer_handle_)
+          << "Failed to initialize an OpenGL renderbuffer!";
+      glBindRenderbuffer(GL_RENDERBUFFER, renderbuffer_handle_);
+      glRenderbufferStorage(GL_RENDERBUFFER, GL_DEPTH_COMPONENT16,
+                            colorbuffer_texture.width(),
+                            colorbuffer_texture.height());
+      glFramebufferRenderbuffer(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
+                                GL_RENDERBUFFER, renderbuffer_handle_);
+      glBindRenderbuffer(GL_RENDERBUFFER, 0);
+    }
+
+    viewport_width_ = colorbuffer_texture.width();
+    viewport_height_ = colorbuffer_texture.height();
+
+    glBindFramebuffer(GL_FRAMEBUFFER, 0);
+    glFlush();
+
+    return absl::OkStatus();
+  }
+
+  void Bind() const {
+    glBindFramebuffer(GL_FRAMEBUFFER, framebuffer_handle_);
+    glViewport(0, 0, viewport_width_, viewport_height_);
+  }
+
+  void Unbind() const { glBindFramebuffer(GL_FRAMEBUFFER, 0); }
+
+  void Clear() const {
+    Bind();
+    glEnable(GL_DEPTH_TEST);
+    glDepthMask(GL_TRUE);
+
+    glClearColor(0.f, 0.f, 0.f, 0.f);
+    glClearDepthf(1.f);
+    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
+
+    glDepthMask(GL_FALSE);
+    glDisable(GL_DEPTH_TEST);
+
+    Unbind();
+    glFlush();
+  }
+
+ private:
+  explicit RenderTarget(GLuint framebuffer_handle)
+      : framebuffer_handle_(framebuffer_handle),
+        renderbuffer_handle_(0),
+        viewport_width_(-1),
+        viewport_height_(-1) {}
+
+  GLuint framebuffer_handle_;
+  GLuint renderbuffer_handle_;
+  int viewport_width_;
+  int viewport_height_;
+};
+
+class Renderer {
+ public:
+  enum class RenderMode { OPAQUE, OVERDRAW, OCCLUSION };
+
+  static absl::StatusOr<std::unique_ptr<Renderer>> Create() {
+    static const GLint kAttrLocation[NUM_ATTRIBUTES] = {
+        ATTRIB_VERTEX,
+        ATTRIB_TEXTURE_POSITION,
+    };
+    static const GLchar* kAttrName[NUM_ATTRIBUTES] = {
+        "position",
+        "tex_coord",
+    };
+
+    static const GLchar* kVertSrc = R"(
+      uniform mat4 projection_mat;
+      uniform mat4 model_mat;
+
+      attribute vec4 position;
+      attribute vec4 tex_coord;
+
+      varying vec2 v_tex_coord;
+
+      void main() {
+        v_tex_coord = tex_coord.xy;
+        gl_Position = projection_mat * model_mat * position;
+      }
+    )";
+
+    static const GLchar* kFragSrc = R"(
+      precision mediump float;
+
+      varying vec2 v_tex_coord;
+      uniform sampler2D texture;
+
+      void main() {
+        gl_FragColor = texture2D(texture, v_tex_coord);
+      }
+    )";
+
+    GLuint program_handle = 0;
+    GlhCreateProgram(kVertSrc, kFragSrc, NUM_ATTRIBUTES,
+                     (const GLchar**)&kAttrName[0], kAttrLocation,
+                     &program_handle);
+    RET_CHECK(program_handle) << "Problem initializing the texture program!";
+    GLint projection_mat_uniform =
+        glGetUniformLocation(program_handle, "projection_mat");
+    GLint model_mat_uniform = glGetUniformLocation(program_handle, "model_mat");
+    GLint texture_uniform = glGetUniformLocation(program_handle, "texture");
+
+    RET_CHECK_NE(projection_mat_uniform, -1)
+        << "Failed to find `projection_mat` uniform!";
+    RET_CHECK_NE(model_mat_uniform, -1)
+        << "Failed to find `model_mat` uniform!";
+    RET_CHECK_NE(texture_uniform, -1) << "Failed to find `texture` uniform!";
+
+    return absl::WrapUnique(new Renderer(program_handle, projection_mat_uniform,
+                                         model_mat_uniform, texture_uniform));
+  }
+
+  ~Renderer() { glDeleteProgram(program_handle_); }
+
+  absl::Status Render(const RenderTarget& render_target, const Texture& texture,
+                      const RenderableMesh3d& mesh_3d,
+                      const std::array<float, 16>& projection_mat,
+                      const std::array<float, 16>& model_mat,
+                      RenderMode render_mode) const {
+    glUseProgram(program_handle_);
+    // Set up the GL state.
+    glEnable(GL_BLEND);
+    glFrontFace(GL_CCW);
+    switch (render_mode) {
+      case RenderMode::OPAQUE:
+        glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);
+        glEnable(GL_DEPTH_TEST);
+        glDepthMask(GL_TRUE);
+        break;
+
+      case RenderMode::OVERDRAW:
+        glBlendFunc(GL_ONE, GL_ZERO);
+        glDisable(GL_DEPTH_TEST);
+        glDepthMask(GL_FALSE);
+        break;
+
+      case RenderMode::OCCLUSION:
+        glBlendFunc(GL_ZERO, GL_ONE);
+        glEnable(GL_DEPTH_TEST);
+        glDepthMask(GL_TRUE);
+        break;
+    }
+
+    render_target.Bind();
+    // Set up vertex attributes.
+    glVertexAttribPointer(
+        ATTRIB_VERTEX, mesh_3d.vertex_position_size, GL_FLOAT, 0,
+        mesh_3d.vertex_size * sizeof(float),
+        mesh_3d.vertex_buffer.data() + mesh_3d.vertex_position_offset);
+    glEnableVertexAttribArray(ATTRIB_VERTEX);
+    glVertexAttribPointer(
+        ATTRIB_TEXTURE_POSITION, mesh_3d.tex_coord_position_size, GL_FLOAT, 0,
+        mesh_3d.vertex_size * sizeof(float),
+        mesh_3d.vertex_buffer.data() + mesh_3d.tex_coord_position_offset);
+    glEnableVertexAttribArray(ATTRIB_TEXTURE_POSITION);
+    // Set up textures and uniforms.
+    glActiveTexture(GL_TEXTURE1);
+    glBindTexture(texture.target(), texture.handle());
+    glUniform1i(texture_uniform_, 1);
+    glUniformMatrix4fv(projection_mat_uniform_, 1, GL_FALSE,
+                       projection_mat.data());
+    glUniformMatrix4fv(model_mat_uniform_, 1, GL_FALSE, model_mat.data());
+    // Draw the mesh.
+    glDrawElements(mesh_3d.primitive_type, mesh_3d.index_buffer.size(),
+                   GL_UNSIGNED_SHORT, mesh_3d.index_buffer.data());
+    // Unbind textures and uniforms.
+    glActiveTexture(GL_TEXTURE1);
+    glBindTexture(texture.target(), 0);
+    render_target.Unbind();
+    // Unbind vertex attributes.
+    glDisableVertexAttribArray(ATTRIB_TEXTURE_POSITION);
+    glDisableVertexAttribArray(ATTRIB_VERTEX);
+    // Restore the GL state.
+    glDepthMask(GL_FALSE);
+    glDisable(GL_DEPTH_TEST);
+    glDisable(GL_BLEND);
+
+    glUseProgram(0);
+    glFlush();
+
+    return absl::OkStatus();
+  }
+
+ private:
+  enum { ATTRIB_VERTEX, ATTRIB_TEXTURE_POSITION, NUM_ATTRIBUTES };
+
+  Renderer(GLuint program_handle, GLint projection_mat_uniform,
+           GLint model_mat_uniform, GLint texture_uniform)
+      : program_handle_(program_handle),
+        projection_mat_uniform_(projection_mat_uniform),
+        model_mat_uniform_(model_mat_uniform),
+        texture_uniform_(texture_uniform) {}
+
+  GLuint program_handle_;
+  GLint projection_mat_uniform_;
+  GLint model_mat_uniform_;
+  GLint texture_uniform_;
+};
+
+class EffectRendererImpl : public EffectRenderer {
+ public:
+  EffectRendererImpl(
+      const Environment& environment,
+      std::unique_ptr<RenderTarget> render_target,
+      std::unique_ptr<Renderer> renderer,
+      RenderableMesh3d&& renderable_quad_mesh_3d,
+      absl::optional<RenderableMesh3d>&& renderable_effect_mesh_3d,
+      std::unique_ptr<Texture> empty_color_texture,
+      std::unique_ptr<Texture> effect_texture)
+      : environment_(environment),
+        render_target_(std::move(render_target)),
+        renderer_(std::move(renderer)),
+        renderable_quad_mesh_3d_(std::move(renderable_quad_mesh_3d)),
+        renderable_effect_mesh_3d_(std::move(renderable_effect_mesh_3d)),
+        empty_color_texture_(std::move(empty_color_texture)),
+        effect_texture_(std::move(effect_texture)),
+        identity_matrix_(Create4x4IdentityMatrix()) {}
+
+  absl::Status RenderEffect(
+      const std::vector<FaceGeometry>& multi_face_geometry,
+      int frame_width,            //
+      int frame_height,           //
+      GLenum src_texture_target,  //
+      GLuint src_texture_name,    //
+      GLenum dst_texture_target,  //
+      GLuint dst_texture_name) {
+    // Validate input arguments.
+    MP_RETURN_IF_ERROR(ValidateFrameDimensions(frame_width, frame_height))
+        << "Invalid frame dimensions!";
+    RET_CHECK(src_texture_name > 0 && dst_texture_name > 0)
+        << "Both source and destination texture names must be non-null!";
+    RET_CHECK_NE(src_texture_name, dst_texture_name)
+        << "Source and destination texture names must be different!";
+
+    // Validate all input face geometries.
+    for (const FaceGeometry& face_geometry : multi_face_geometry) {
+      MP_RETURN_IF_ERROR(ValidateFaceGeometry(face_geometry))
+          << "Invalid face geometry!";
+    }
+
+    // Wrap both source and destination textures.
+    ASSIGN_OR_RETURN(
+        std::unique_ptr<Texture> src_texture,
+        Texture::WrapExternalTexture(src_texture_name, src_texture_target,
+                                     frame_width, frame_height),
+        _ << "Failed to wrap the external source texture");
+    ASSIGN_OR_RETURN(
+        std::unique_ptr<Texture> dst_texture,
+        Texture::WrapExternalTexture(dst_texture_name, dst_texture_target,
+                                     frame_width, frame_height),
+        _ << "Failed to wrap the external destination texture");
+
+    // Set the destination texture as the color buffer. Then, clear both the
+    // color and the depth buffers for the render target.
+    MP_RETURN_IF_ERROR(render_target_->SetColorbuffer(*dst_texture))
+        << "Failed to set the destination texture as the colorbuffer!";
+    render_target_->Clear();
+
+    // Render the source texture on top of the quad mesh (i.e. make a copy)
+    // into the render target.
+    MP_RETURN_IF_ERROR(renderer_->Render(
+        *render_target_, *src_texture, renderable_quad_mesh_3d_,
+        identity_matrix_, identity_matrix_, Renderer::RenderMode::OVERDRAW))
+        << "Failed to render the source texture on top of the quad mesh!";
+
+    // Extract pose transform matrices and meshes from the face geometry data;
+    const int num_faces = multi_face_geometry.size();
+
+    std::vector<std::array<float, 16>> face_pose_transform_matrices(num_faces);
+    std::vector<RenderableMesh3d> renderable_face_meshes(num_faces);
+    for (int i = 0; i < num_faces; ++i) {
+      const FaceGeometry& face_geometry = multi_face_geometry[i];
+
+      // Extract the face pose transformation matrix.
+      ASSIGN_OR_RETURN(
+          face_pose_transform_matrices[i],
+          Convert4x4MatrixDataToArrayFormat(
+              face_geometry.pose_transform_matrix()),
+          _ << "Failed to extract the face pose transformation matrix!");
+
+      // Extract the face mesh as a renderable.
+      ASSIGN_OR_RETURN(
+          renderable_face_meshes[i],
+          RenderableMesh3d::CreateFromProtoMesh3d(face_geometry.mesh()),
+          _ << "Failed to extract a renderable face mesh!");
+    }
+
+    // Create a perspective matrix using the frame aspect ratio.
+    std::array<float, 16> perspective_matrix = CreatePerspectiveMatrix(
+        /*aspect_ratio*/ static_cast<float>(frame_width) / frame_height);
+
+    // Render a face mesh occluder for each face.
+    for (int i = 0; i < num_faces; ++i) {
+      const std::array<float, 16>& face_pose_transform_matrix =
+          face_pose_transform_matrices[i];
+      const RenderableMesh3d& renderable_face_mesh = renderable_face_meshes[i];
+
+      // Render the face mesh using the empty color texture, i.e. the face
+      // mesh occluder.
+      //
+      // For occlusion, the pose transformation is moved ~1mm away from camera
+      // in order to allow the face mesh texture to be rendered without
+      // failing the depth test.
+      std::array<float, 16> occlusion_face_pose_transform_matrix =
+          face_pose_transform_matrix;
+      occlusion_face_pose_transform_matrix[14] -= 0.1f;  // ~ 1mm
+      MP_RETURN_IF_ERROR(renderer_->Render(
+          *render_target_, *empty_color_texture_, renderable_face_mesh,
+          perspective_matrix, occlusion_face_pose_transform_matrix,
+          Renderer::RenderMode::OCCLUSION))
+          << "Failed to render the face mesh occluder!";
+    }
+
+    // Render the main face mesh effect component for each face.
+    for (int i = 0; i < num_faces; ++i) {
+      const std::array<float, 16>& face_pose_transform_matrix =
+          face_pose_transform_matrices[i];
+
+      // If there is no effect 3D mesh provided, then the face mesh itself is
+      // used as a topology for rendering (for example, this can be used for
+      // facepaint effects or AR makeup).
+      const RenderableMesh3d& main_effect_mesh_3d =
+          renderable_effect_mesh_3d_ ? *renderable_effect_mesh_3d_
+                                     : renderable_face_meshes[i];
+
+      MP_RETURN_IF_ERROR(renderer_->Render(
+          *render_target_, *effect_texture_, main_effect_mesh_3d,
+          perspective_matrix, face_pose_transform_matrix,
+          Renderer::RenderMode::OPAQUE))
+          << "Failed to render the main effect pass!";
+    }
+
+    // At this point in the code, the destination texture must contain the
+    // correctly renderer effect, so we should just return.
+    return absl::OkStatus();
+  }
+
+ private:
+  std::array<float, 16> CreatePerspectiveMatrix(float aspect_ratio) const {
+    static constexpr float kDegreesToRadians = M_PI / 180.f;
+
+    std::array<float, 16> perspective_matrix;
+    perspective_matrix.fill(0.f);
+
+    const auto& env_camera = environment_.perspective_camera();
+    // Standard perspective projection matrix calculations.
+    const float f = 1.0f / std::tan(kDegreesToRadians *
+                                    env_camera.vertical_fov_degrees() / 2.f);
+
+    const float denom = 1.0f / (env_camera.near() - env_camera.far());
+    perspective_matrix[0] = f / aspect_ratio;
+    perspective_matrix[5] = f;
+    perspective_matrix[10] = (env_camera.near() + env_camera.far()) * denom;
+    perspective_matrix[11] = -1.f;
+    perspective_matrix[14] = 2.f * env_camera.far() * env_camera.near() * denom;
+
+    // If the environment's origin point location is in the top left corner,
+    // then skip additional flip along Y-axis is required to render correctly.
+    if (environment_.origin_point_location() ==
+        OriginPointLocation::TOP_LEFT_CORNER) {
+      perspective_matrix[5] *= -1.f;
+    }
+
+    return perspective_matrix;
+  }
+
+  static std::array<float, 16> Create4x4IdentityMatrix() {
+    return {1.f, 0.f, 0.f, 0.f,  //
+            0.f, 1.f, 0.f, 0.f,  //
+            0.f, 0.f, 1.f, 0.f,  //
+            0.f, 0.f, 0.f, 1.f};
+  }
+
+  static absl::StatusOr<std::array<float, 16>>
+  Convert4x4MatrixDataToArrayFormat(const MatrixData& matrix_data) {
+    RET_CHECK(matrix_data.rows() == 4 &&  //
+              matrix_data.cols() == 4 &&  //
+              matrix_data.packed_data_size() == 16)
+        << "The matrix data must define a 4x4 matrix!";
+
+    std::array<float, 16> matrix_array;
+    for (int i = 0; i < 16; i++) {
+      matrix_array[i] = matrix_data.packed_data(i);
+    }
+
+    // Matrix array must be in the OpenGL-friendly column-major order. If
+    // `matrix_data` is in the row-major order, then transpose.
+    if (matrix_data.layout() == MatrixData::ROW_MAJOR) {
+      std::swap(matrix_array[1], matrix_array[4]);
+      std::swap(matrix_array[2], matrix_array[8]);
+      std::swap(matrix_array[3], matrix_array[12]);
+      std::swap(matrix_array[6], matrix_array[9]);
+      std::swap(matrix_array[7], matrix_array[13]);
+      std::swap(matrix_array[11], matrix_array[14]);
+    }
+
+    return matrix_array;
+  }
+
+  Environment environment_;
+
+  std::unique_ptr<RenderTarget> render_target_;
+  std::unique_ptr<Renderer> renderer_;
+
+  RenderableMesh3d renderable_quad_mesh_3d_;
+  absl::optional<RenderableMesh3d> renderable_effect_mesh_3d_;
+
+  std::unique_ptr<Texture> empty_color_texture_;
+  std::unique_ptr<Texture> effect_texture_;
+
+  std::array<float, 16> identity_matrix_;
+};
+
+Mesh3d CreateQuadMesh3d() {
+  static constexpr float kQuadMesh3dVertexBuffer[] = {
+      -1.f, -1.f, 0.f, 0.f, 0.f,  //
+      1.f,  -1.f, 0.f, 1.f, 0.f,  //
+      -1.f, 1.f,  0.f, 0.f, 1.f,  //
+      1.f,  1.f,  0.f, 1.f, 1.f,  //
+  };
+  static constexpr uint16_t kQuadMesh3dIndexBuffer[] = {0, 1, 2, 1, 3, 2};
+
+  static constexpr int kQuadMesh3dVertexBufferSize =
+      sizeof(kQuadMesh3dVertexBuffer) / sizeof(float);
+  static constexpr int kQuadMesh3dIndexBufferSize =
+      sizeof(kQuadMesh3dIndexBuffer) / sizeof(uint16_t);
+
+  Mesh3d quad_mesh_3d;
+  quad_mesh_3d.set_vertex_type(Mesh3d::VERTEX_PT);
+  quad_mesh_3d.set_primitive_type(Mesh3d::TRIANGLE);
+  for (int i = 0; i < kQuadMesh3dVertexBufferSize; ++i) {
+    quad_mesh_3d.add_vertex_buffer(kQuadMesh3dVertexBuffer[i]);
+  }
+  for (int i = 0; i < kQuadMesh3dIndexBufferSize; ++i) {
+    quad_mesh_3d.add_index_buffer(kQuadMesh3dIndexBuffer[i]);
+  }
+
+  return quad_mesh_3d;
+}
+
+ImageFrame CreateEmptyColorTexture() {
+  static constexpr ImageFormat::Format kEmptyColorTextureFormat =
+      ImageFormat::SRGBA;
+  static constexpr int kEmptyColorTextureWidth = 1;
+  static constexpr int kEmptyColorTextureHeight = 1;
+
+  ImageFrame empty_color_texture(
+      kEmptyColorTextureFormat, kEmptyColorTextureWidth,
+      kEmptyColorTextureHeight, ImageFrame::kGlDefaultAlignmentBoundary);
+  empty_color_texture.SetToZero();
+
+  return empty_color_texture;
+}
+
+}  // namespace
+
+absl::StatusOr<std::unique_ptr<EffectRenderer>> CreateEffectRenderer(
+    const Environment& environment,                //
+    const absl::optional<Mesh3d>& effect_mesh_3d,  //
+    ImageFrame&& effect_texture) {
+  MP_RETURN_IF_ERROR(ValidateEnvironment(environment))
+      << "Invalid environment!";
+  if (effect_mesh_3d) {
+    MP_RETURN_IF_ERROR(ValidateMesh3d(*effect_mesh_3d))
+        << "Invalid effect 3D mesh!";
+  }
+
+  ASSIGN_OR_RETURN(std::unique_ptr<RenderTarget> render_target,
+                   RenderTarget::Create(),
+                   _ << "Failed to create a render target!");
+  ASSIGN_OR_RETURN(std::unique_ptr<Renderer> renderer, Renderer::Create(),
+                   _ << "Failed to create a renderer!");
+  ASSIGN_OR_RETURN(RenderableMesh3d renderable_quad_mesh_3d,
+                   RenderableMesh3d::CreateFromProtoMesh3d(CreateQuadMesh3d()),
+                   _ << "Failed to create a renderable quad mesh!");
+  absl::optional<RenderableMesh3d> renderable_effect_mesh_3d;
+  if (effect_mesh_3d) {
+    ASSIGN_OR_RETURN(renderable_effect_mesh_3d,
+                     RenderableMesh3d::CreateFromProtoMesh3d(*effect_mesh_3d),
+                     _ << "Failed to create a renderable effect mesh!");
+  }
+  ASSIGN_OR_RETURN(std::unique_ptr<Texture> empty_color_gl_texture,
+                   Texture::CreateFromImageFrame(CreateEmptyColorTexture()),
+                   _ << "Failed to create an empty color texture!");
+  ASSIGN_OR_RETURN(std::unique_ptr<Texture> effect_gl_texture,
+                   Texture::CreateFromImageFrame(effect_texture),
+                   _ << "Failed to create an effect texture!");
+
+  std::unique_ptr<EffectRenderer> result =
+      absl::make_unique<EffectRendererImpl>(
+          environment, std::move(render_target), std::move(renderer),
+          std::move(renderable_quad_mesh_3d),
+          std::move(renderable_effect_mesh_3d),
+          std::move(empty_color_gl_texture), std::move(effect_gl_texture));
+
+  return result;
+}
+
+}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/effect_renderer.h b/mediapipe/modules/face_geometry/libs/effect_renderer.h
new file mode 100644
index 0000000..71330e7
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/effect_renderer.h
@@ -0,0 +1,92 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_FACE_GEOMETRY_LIBS_EFFECT_RENDERER_H_
+#define MEDIAPIPE_MODULES_FACE_GEOMETRY_LIBS_EFFECT_RENDERER_H_
+
+#include <memory>
+#include <vector>
+
+#include "absl/types/optional.h"
+#include "mediapipe/framework/formats/image_frame.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/gpu/gl_base.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+
+namespace mediapipe::face_geometry {
+
+// Encapsulates a stateful face effect renderer.
+class EffectRenderer {
+ public:
+  virtual ~EffectRenderer() = default;
+
+  // Renders a face effect based on the multiple facial geometries.
+  //
+  // Must be called in the same GL context as was used upon initialization.
+  //
+  // Each of the `multi_face_geometry` must be valid (for details, please refer
+  // to the proto message definition comments and/or `validation_utils.h/cc`).
+  // Additionally, all face mesh index buffer elements must fit into the
+  // `uint16` type in order to be renderable.
+  //
+  // Both `frame_width` and `frame_height` must be positive.
+  //
+  // Both `src_texture_name` and `dst_texture_name` must be positive and
+  // reference existing OpenGL textures in the current context. They should also
+  // reference different textures as the in-place effect rendering is not yet
+  // supported.
+  virtual absl::Status RenderEffect(
+      const std::vector<FaceGeometry>& multi_face_geometry,
+      int frame_width,            //
+      int frame_height,           //
+      GLenum src_texture_target,  //
+      GLuint src_texture_name,    //
+      GLenum dst_texture_target,  //
+      GLuint dst_texture_name) = 0;
+};
+
+// Creates an instance of `EffectRenderer`.
+//
+// `effect_mesh_3d` defines a rigid 3d mesh which is "attached" to the face and
+// is driven by the face pose transformation matrix. If is not present, the
+// runtime face mesh will be used as the effect mesh - this mode is handy for
+// facepaint effects. In both rendering modes, the face mesh is first rendered
+// as an occluder straight into the depth buffer. This step helps to create a
+// more believable effect via hiding invisible elements behind the face surface.
+//
+// `effect_texture` defines the color texture to be rendered on top of the
+// effect mesh. Please be aware about the difference between the CPU texture
+// memory layout and the GPU texture sampler coordinate space. This renderer
+// follows conventions discussed here: https://open.gl/textures
+//
+// Must be called in the same GL context as will be used for rendering.
+//
+// Both `environment` and `effect_mesh_3d` (is present) must be valid (for
+// details, please refer to the proto message definition comments and/or
+// `validation_utils.h/cc`). Additionally, `effect_mesh_3d`s index buffer
+// elements must fit into the `uint16` type in order to be renderable.
+//
+// `effect_texture` must have positive dimensions. Its format must be either
+// `SRGB` or `SRGBA`. Its memory must be aligned for GL usage.
+absl::StatusOr<std::unique_ptr<EffectRenderer>> CreateEffectRenderer(
+    const Environment& environment,                //
+    const absl::optional<Mesh3d>& effect_mesh_3d,  //
+    ImageFrame&& effect_texture);
+
+}  // namespace mediapipe::face_geometry
+
+#endif  // MEDIAPIPE_MODULES_FACE_GEOMETRY_LIBS_EFFECT_RENDERER_H_
diff --git a/mediapipe/modules/face_geometry/libs/geometry_pipeline.cc b/mediapipe/modules/face_geometry/libs/geometry_pipeline.cc
new file mode 100644
index 0000000..bcfce7c
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/geometry_pipeline.cc
@@ -0,0 +1,466 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/face_geometry/libs/geometry_pipeline.h"
+
+#include <cmath>
+#include <cstdint>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "Eigen/Core"
+#include "absl/memory/memory.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/formats/matrix.h"
+#include "mediapipe/framework/formats/matrix_data.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_macros.h"
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
+#include "mediapipe/modules/face_geometry/libs/procrustes_solver.h"
+#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
+#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+
+namespace mediapipe::face_geometry {
+namespace {
+
+struct PerspectiveCameraFrustum {
+  // NOTE: all arguments must be validated prior to calling this constructor.
+  PerspectiveCameraFrustum(const PerspectiveCamera& perspective_camera,
+                           int frame_width, int frame_height) {
+    static constexpr float kDegreesToRadians = 3.14159265358979323846f / 180.f;
+
+    const float height_at_near =
+        2.f * perspective_camera.near() *
+        std::tan(0.5f * kDegreesToRadians *
+                 perspective_camera.vertical_fov_degrees());
+
+    const float width_at_near = frame_width * height_at_near / frame_height;
+
+    left = -0.5f * width_at_near;
+    right = 0.5f * width_at_near;
+    bottom = -0.5f * height_at_near;
+    top = 0.5f * height_at_near;
+    near = perspective_camera.near();
+    far = perspective_camera.far();
+  }
+
+  float left;
+  float right;
+  float bottom;
+  float top;
+  float near;
+  float far;
+};
+
+class ScreenToMetricSpaceConverter {
+ public:
+  ScreenToMetricSpaceConverter(
+      OriginPointLocation origin_point_location,      //
+      InputSource input_source,                       //
+      Eigen::Matrix3Xf&& canonical_metric_landmarks,  //
+      Eigen::VectorXf&& landmark_weights,             //
+      std::unique_ptr<ProcrustesSolver> procrustes_solver)
+      : origin_point_location_(origin_point_location),
+        input_source_(input_source),
+        canonical_metric_landmarks_(std::move(canonical_metric_landmarks)),
+        landmark_weights_(std::move(landmark_weights)),
+        procrustes_solver_(std::move(procrustes_solver)) {}
+
+  // Converts `screen_landmark_list` into `metric_landmark_list` and estimates
+  // the `pose_transform_mat`.
+  //
+  // Here's the algorithm summary:
+  //
+  // (1) Project X- and Y- screen landmark coordinates at the Z near plane.
+  //
+  // (2) Estimate a canonical-to-runtime landmark set scale by running the
+  //     Procrustes solver using the screen runtime landmarks.
+  //
+  //     On this iteration, screen landmarks are used instead of unprojected
+  //     metric landmarks as it is not safe to unproject due to the relative
+  //     nature of the input screen landmark Z coordinate.
+  //
+  // (3) Use the canonical-to-runtime scale from (2) to unproject the screen
+  //     landmarks. The result is referenced as "intermediate landmarks" because
+  //     they are the first estimation of the resuling metric landmarks, but are
+  //     not quite there yet.
+  //
+  // (4) Estimate a canonical-to-runtime landmark set scale by running the
+  //     Procrustes solver using the intermediate runtime landmarks.
+  //
+  // (5) Use the product of the scale factors from (2) and (4) to unproject
+  //     the screen landmarks the second time. This is the second and the final
+  //     estimation of the metric landmarks.
+  //
+  // (6) Multiply each of the metric landmarks by the inverse pose
+  //     transformation matrix to align the runtime metric face landmarks with
+  //     the canonical metric face landmarks.
+  //
+  // Note: the input screen landmarks are in the left-handed coordinate system,
+  //       however any metric landmarks - including the canonical metric
+  //       landmarks, the final runtime metric landmarks and any intermediate
+  //       runtime metric landmarks - are in the right-handed coordinate system.
+  //
+  //       To keep the logic correct, the landmark set handedness is changed any
+  //       time the screen-to-metric semantic barrier is passed.
+  absl::Status Convert(const NormalizedLandmarkList& screen_landmark_list,  //
+                       const PerspectiveCameraFrustum& pcf,                 //
+                       LandmarkList& metric_landmark_list,                  //
+                       Eigen::Matrix4f& pose_transform_mat) const {
+    RET_CHECK_EQ(screen_landmark_list.landmark_size(),
+                 canonical_metric_landmarks_.cols())
+        << "The number of landmarks doesn't match the number passed upon "
+           "initialization!";
+
+    Eigen::Matrix3Xf screen_landmarks;
+    ConvertLandmarkListToEigenMatrix(screen_landmark_list, screen_landmarks);
+
+    ProjectXY(pcf, screen_landmarks);
+    const float depth_offset = screen_landmarks.row(2).mean();
+
+    // 1st iteration: don't unproject XY because it's unsafe to do so due to
+    //                the relative nature of the Z coordinate. Instead, run the
+    //                first estimation on the projected XY and use that scale to
+    //                unproject for the 2nd iteration.
+    Eigen::Matrix3Xf intermediate_landmarks(screen_landmarks);
+    ChangeHandedness(intermediate_landmarks);
+
+    ASSIGN_OR_RETURN(const float first_iteration_scale,
+                     EstimateScale(intermediate_landmarks),
+                     _ << "Failed to estimate first iteration scale!");
+
+    // 2nd iteration: unproject XY using the scale from the 1st iteration.
+    intermediate_landmarks = screen_landmarks;
+    MoveAndRescaleZ(pcf, depth_offset, first_iteration_scale,
+                    intermediate_landmarks);
+    UnprojectXY(pcf, intermediate_landmarks);
+    ChangeHandedness(intermediate_landmarks);
+
+    // For face detection input landmarks, re-write Z-coord from the canonical
+    // landmarks.
+    if (input_source_ == InputSource::FACE_DETECTION_PIPELINE) {
+      Eigen::Matrix4f intermediate_pose_transform_mat;
+      MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
+          canonical_metric_landmarks_, intermediate_landmarks,
+          landmark_weights_, intermediate_pose_transform_mat))
+          << "Failed to estimate pose transform matrix!";
+
+      intermediate_landmarks.row(2) =
+          (intermediate_pose_transform_mat *
+           canonical_metric_landmarks_.colwise().homogeneous())
+              .row(2);
+    }
+    ASSIGN_OR_RETURN(const float second_iteration_scale,
+                     EstimateScale(intermediate_landmarks),
+                     _ << "Failed to estimate second iteration scale!");
+
+    // Use the total scale to unproject the screen landmarks.
+    const float total_scale = first_iteration_scale * second_iteration_scale;
+    MoveAndRescaleZ(pcf, depth_offset, total_scale, screen_landmarks);
+    UnprojectXY(pcf, screen_landmarks);
+    ChangeHandedness(screen_landmarks);
+
+    // At this point, screen landmarks are converted into metric landmarks.
+    Eigen::Matrix3Xf& metric_landmarks = screen_landmarks;
+
+    MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
+        canonical_metric_landmarks_, metric_landmarks, landmark_weights_,
+        pose_transform_mat))
+        << "Failed to estimate pose transform matrix!";
+
+    // For face detection input landmarks, re-write Z-coord from the canonical
+    // landmarks and run the pose transform estimation again.
+    if (input_source_ == InputSource::FACE_DETECTION_PIPELINE) {
+      metric_landmarks.row(2) =
+          (pose_transform_mat *
+           canonical_metric_landmarks_.colwise().homogeneous())
+              .row(2);
+
+      MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
+          canonical_metric_landmarks_, metric_landmarks, landmark_weights_,
+          pose_transform_mat))
+          << "Failed to estimate pose transform matrix!";
+    }
+
+    // Multiply each of the metric landmarks by the inverse pose
+    // transformation matrix to align the runtime metric face landmarks with
+    // the canonical metric face landmarks.
+    metric_landmarks = (pose_transform_mat.inverse() *
+                        metric_landmarks.colwise().homogeneous())
+                           .topRows(3);
+
+    ConvertEigenMatrixToLandmarkList(metric_landmarks, metric_landmark_list);
+
+    return absl::OkStatus();
+  }
+
+ private:
+  void ProjectXY(const PerspectiveCameraFrustum& pcf,
+                 Eigen::Matrix3Xf& landmarks) const {
+    float x_scale = pcf.right - pcf.left;
+    float y_scale = pcf.top - pcf.bottom;
+    float x_translation = pcf.left;
+    float y_translation = pcf.bottom;
+
+    if (origin_point_location_ == OriginPointLocation::TOP_LEFT_CORNER) {
+      landmarks.row(1) = 1.f - landmarks.row(1).array();
+    }
+
+    landmarks =
+        landmarks.array().colwise() * Eigen::Array3f(x_scale, y_scale, x_scale);
+    landmarks.colwise() += Eigen::Vector3f(x_translation, y_translation, 0.f);
+  }
+
+  absl::StatusOr<float> EstimateScale(Eigen::Matrix3Xf& landmarks) const {
+    Eigen::Matrix4f transform_mat;
+    MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
+        canonical_metric_landmarks_, landmarks, landmark_weights_,
+        transform_mat))
+        << "Failed to estimate canonical-to-runtime landmark set transform!";
+
+    return transform_mat.col(0).norm();
+  }
+
+  static void MoveAndRescaleZ(const PerspectiveCameraFrustum& pcf,
+                              float depth_offset, float scale,
+                              Eigen::Matrix3Xf& landmarks) {
+    landmarks.row(2) =
+        (landmarks.array().row(2) - depth_offset + pcf.near) / scale;
+  }
+
+  static void UnprojectXY(const PerspectiveCameraFrustum& pcf,
+                          Eigen::Matrix3Xf& landmarks) {
+    landmarks.row(0) =
+        landmarks.row(0).cwiseProduct(landmarks.row(2)) / pcf.near;
+    landmarks.row(1) =
+        landmarks.row(1).cwiseProduct(landmarks.row(2)) / pcf.near;
+  }
+
+  static void ChangeHandedness(Eigen::Matrix3Xf& landmarks) {
+    landmarks.row(2) *= -1.f;
+  }
+
+  static void ConvertLandmarkListToEigenMatrix(
+      const NormalizedLandmarkList& landmark_list,
+      Eigen::Matrix3Xf& eigen_matrix) {
+    eigen_matrix = Eigen::Matrix3Xf(3, landmark_list.landmark_size());
+    for (int i = 0; i < landmark_list.landmark_size(); ++i) {
+      const auto& landmark = landmark_list.landmark(i);
+      eigen_matrix(0, i) = landmark.x();
+      eigen_matrix(1, i) = landmark.y();
+      eigen_matrix(2, i) = landmark.z();
+    }
+  }
+
+  static void ConvertEigenMatrixToLandmarkList(
+      const Eigen::Matrix3Xf& eigen_matrix, LandmarkList& landmark_list) {
+    landmark_list.Clear();
+
+    for (int i = 0; i < eigen_matrix.cols(); ++i) {
+      auto& landmark = *landmark_list.add_landmark();
+      landmark.set_x(eigen_matrix(0, i));
+      landmark.set_y(eigen_matrix(1, i));
+      landmark.set_z(eigen_matrix(2, i));
+    }
+  }
+
+  const OriginPointLocation origin_point_location_;
+  const InputSource input_source_;
+  Eigen::Matrix3Xf canonical_metric_landmarks_;
+  Eigen::VectorXf landmark_weights_;
+
+  std::unique_ptr<ProcrustesSolver> procrustes_solver_;
+};
+
+class GeometryPipelineImpl : public GeometryPipeline {
+ public:
+  GeometryPipelineImpl(
+      const PerspectiveCamera& perspective_camera,  //
+      const Mesh3d& canonical_mesh,                 //
+      uint32_t canonical_mesh_vertex_size,          //
+      uint32_t canonical_mesh_num_vertices,
+      uint32_t canonical_mesh_vertex_position_offset,
+      std::unique_ptr<ScreenToMetricSpaceConverter> space_converter)
+      : perspective_camera_(perspective_camera),
+        canonical_mesh_(canonical_mesh),
+        canonical_mesh_vertex_size_(canonical_mesh_vertex_size),
+        canonical_mesh_num_vertices_(canonical_mesh_num_vertices),
+        canonical_mesh_vertex_position_offset_(
+            canonical_mesh_vertex_position_offset),
+        space_converter_(std::move(space_converter)) {}
+
+  absl::StatusOr<std::vector<FaceGeometry>> EstimateFaceGeometry(
+      const std::vector<NormalizedLandmarkList>& multi_face_landmarks,
+      int frame_width, int frame_height) const override {
+    MP_RETURN_IF_ERROR(ValidateFrameDimensions(frame_width, frame_height))
+        << "Invalid frame dimensions!";
+
+    // Create a perspective camera frustum to be shared for geometry estimation
+    // per each face.
+    PerspectiveCameraFrustum pcf(perspective_camera_, frame_width,
+                                 frame_height);
+
+    std::vector<FaceGeometry> multi_face_geometry;
+
+    // From this point, the meaning of "face landmarks" is clarified further as
+    // "screen face landmarks". This is done do distinguish from "metric face
+    // landmarks" that are derived during the face geometry estimation process.
+    for (const NormalizedLandmarkList& screen_face_landmarks :
+         multi_face_landmarks) {
+      // Having a too compact screen landmark list will result in numerical
+      // instabilities, therefore such faces are filtered.
+      if (IsScreenLandmarkListTooCompact(screen_face_landmarks)) {
+        continue;
+      }
+
+      // Convert the screen landmarks into the metric landmarks and get the pose
+      // transformation matrix.
+      LandmarkList metric_face_landmarks;
+      Eigen::Matrix4f pose_transform_mat;
+      MP_RETURN_IF_ERROR(space_converter_->Convert(screen_face_landmarks, pcf,
+                                                   metric_face_landmarks,
+                                                   pose_transform_mat))
+          << "Failed to convert landmarks from the screen to the metric space!";
+
+      // Pack geometry data for this face.
+      FaceGeometry face_geometry;
+      Mesh3d* mutable_mesh = face_geometry.mutable_mesh();
+      // Copy the canonical face mesh as the face geometry mesh.
+      mutable_mesh->CopyFrom(canonical_mesh_);
+      // Replace XYZ vertex mesh coodinates with the metric landmark positions.
+      for (int i = 0; i < canonical_mesh_num_vertices_; ++i) {
+        uint32_t vertex_buffer_offset = canonical_mesh_vertex_size_ * i +
+                                        canonical_mesh_vertex_position_offset_;
+
+        mutable_mesh->set_vertex_buffer(vertex_buffer_offset,
+                                        metric_face_landmarks.landmark(i).x());
+        mutable_mesh->set_vertex_buffer(vertex_buffer_offset + 1,
+                                        metric_face_landmarks.landmark(i).y());
+        mutable_mesh->set_vertex_buffer(vertex_buffer_offset + 2,
+                                        metric_face_landmarks.landmark(i).z());
+      }
+      // Populate the face pose transformation matrix.
+      mediapipe::MatrixDataProtoFromMatrix(
+          pose_transform_mat, face_geometry.mutable_pose_transform_matrix());
+
+      multi_face_geometry.push_back(face_geometry);
+    }
+
+    return multi_face_geometry;
+  }
+
+ private:
+  static bool IsScreenLandmarkListTooCompact(
+      const NormalizedLandmarkList& screen_landmarks) {
+    float mean_x = 0.f;
+    float mean_y = 0.f;
+    for (int i = 0; i < screen_landmarks.landmark_size(); ++i) {
+      const auto& landmark = screen_landmarks.landmark(i);
+      mean_x += (landmark.x() - mean_x) / static_cast<float>(i + 1);
+      mean_y += (landmark.y() - mean_y) / static_cast<float>(i + 1);
+    }
+
+    float max_sq_dist = 0.f;
+    for (const auto& landmark : screen_landmarks.landmark()) {
+      const float d_x = landmark.x() - mean_x;
+      const float d_y = landmark.y() - mean_y;
+      max_sq_dist = std::max(max_sq_dist, d_x * d_x + d_y * d_y);
+    }
+
+    static constexpr float kIsScreenLandmarkListTooCompactThreshold = 1e-3f;
+    return std::sqrt(max_sq_dist) <= kIsScreenLandmarkListTooCompactThreshold;
+  }
+
+  const PerspectiveCamera perspective_camera_;
+  const Mesh3d canonical_mesh_;
+  const uint32_t canonical_mesh_vertex_size_;
+  const uint32_t canonical_mesh_num_vertices_;
+  const uint32_t canonical_mesh_vertex_position_offset_;
+
+  std::unique_ptr<ScreenToMetricSpaceConverter> space_converter_;
+};
+
+}  // namespace
+
+absl::StatusOr<std::unique_ptr<GeometryPipeline>> CreateGeometryPipeline(
+    const Environment& environment, const GeometryPipelineMetadata& metadata) {
+  MP_RETURN_IF_ERROR(ValidateEnvironment(environment))
+      << "Invalid environment!";
+  MP_RETURN_IF_ERROR(ValidateGeometryPipelineMetadata(metadata))
+      << "Invalid geometry pipeline metadata!";
+
+  const auto& canonical_mesh = metadata.canonical_mesh();
+  RET_CHECK(HasVertexComponent(canonical_mesh.vertex_type(),
+                               VertexComponent::POSITION))
+      << "Canonical face mesh must have the `POSITION` vertex component!";
+  RET_CHECK(HasVertexComponent(canonical_mesh.vertex_type(),
+                               VertexComponent::TEX_COORD))
+      << "Canonical face mesh must have the `TEX_COORD` vertex component!";
+
+  uint32_t canonical_mesh_vertex_size =
+      GetVertexSize(canonical_mesh.vertex_type());
+  uint32_t canonical_mesh_num_vertices =
+      canonical_mesh.vertex_buffer_size() / canonical_mesh_vertex_size;
+  uint32_t canonical_mesh_vertex_position_offset =
+      GetVertexComponentOffset(canonical_mesh.vertex_type(),
+                               VertexComponent::POSITION)
+          .value();
+
+  // Put the Procrustes landmark basis into Eigen matrices for an easier access.
+  Eigen::Matrix3Xf canonical_metric_landmarks =
+      Eigen::Matrix3Xf::Zero(3, canonical_mesh_num_vertices);
+  Eigen::VectorXf landmark_weights =
+      Eigen::VectorXf::Zero(canonical_mesh_num_vertices);
+
+  for (int i = 0; i < canonical_mesh_num_vertices; ++i) {
+    uint32_t vertex_buffer_offset =
+        canonical_mesh_vertex_size * i + canonical_mesh_vertex_position_offset;
+
+    canonical_metric_landmarks(0, i) =
+        canonical_mesh.vertex_buffer(vertex_buffer_offset);
+    canonical_metric_landmarks(1, i) =
+        canonical_mesh.vertex_buffer(vertex_buffer_offset + 1);
+    canonical_metric_landmarks(2, i) =
+        canonical_mesh.vertex_buffer(vertex_buffer_offset + 2);
+  }
+
+  for (const WeightedLandmarkRef& wlr : metadata.procrustes_landmark_basis()) {
+    uint32_t landmark_id = wlr.landmark_id();
+    landmark_weights(landmark_id) = wlr.weight();
+  }
+
+  std::unique_ptr<GeometryPipeline> result =
+      absl::make_unique<GeometryPipelineImpl>(
+          environment.perspective_camera(), canonical_mesh,
+          canonical_mesh_vertex_size, canonical_mesh_num_vertices,
+          canonical_mesh_vertex_position_offset,
+          absl::make_unique<ScreenToMetricSpaceConverter>(
+              environment.origin_point_location(),
+              metadata.input_source() == InputSource::DEFAULT
+                  ? InputSource::FACE_LANDMARK_PIPELINE
+                  : metadata.input_source(),
+              std::move(canonical_metric_landmarks),
+              std::move(landmark_weights),
+              CreateFloatPrecisionProcrustesSolver()));
+
+  return result;
+}
+
+}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/geometry_pipeline.h b/mediapipe/modules/face_geometry/libs/geometry_pipeline.h
new file mode 100644
index 0000000..ffa779c
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/geometry_pipeline.h
@@ -0,0 +1,67 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_GEOMETRY_PIPELINE_H_
+#define MEDIAPIPE_FACE_GEOMETRY_LIBS_GEOMETRY_PIPELINE_H_
+
+#include <memory>
+#include <vector>
+
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
+#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
+
+namespace mediapipe::face_geometry {
+
+// Encapsulates a stateless estimator of facial geometry in a Metric space based
+// on the normalized face landmarks in the Screen space.
+class GeometryPipeline {
+ public:
+  virtual ~GeometryPipeline() = default;
+
+  // Estimates geometry data for multiple faces.
+  //
+  // Returns an error status if any of the passed arguments is invalid.
+  //
+  // The result includes face geometry data for a subset of the input faces,
+  // however geometry data for some faces might be missing. This may happen if
+  // it'd be unstable to estimate the facial geometry based on a corresponding
+  // face landmark list for any reason (for example, if the landmark list is too
+  // compact).
+  //
+  // Each face landmark list must have the same number of landmarks as was
+  // passed upon initialization via the canonical face mesh (as a part of the
+  // geometry pipeline metadata).
+  //
+  // Both `frame_width` and `frame_height` must be positive.
+  virtual absl::StatusOr<std::vector<FaceGeometry>> EstimateFaceGeometry(
+      const std::vector<NormalizedLandmarkList>& multi_face_landmarks,
+      int frame_width, int frame_height) const = 0;
+};
+
+// Creates an instance of `GeometryPipeline`.
+//
+// Both `environment` and `metadata` must be valid (for details, please refer to
+// the proto message definition comments and/or `validation_utils.h/cc`).
+//
+// Canonical face mesh (defined as a part of `metadata`) must have the
+// `POSITION` and the `TEX_COORD` vertex components.
+absl::StatusOr<std::unique_ptr<GeometryPipeline>> CreateGeometryPipeline(
+    const Environment& environment, const GeometryPipelineMetadata& metadata);
+
+}  // namespace mediapipe::face_geometry
+
+#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_GEOMETRY_PIPELINE_H_
diff --git a/mediapipe/modules/face_geometry/libs/mesh_3d_utils.cc b/mediapipe/modules/face_geometry/libs/mesh_3d_utils.cc
new file mode 100644
index 0000000..2078ec6
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/mesh_3d_utils.cc
@@ -0,0 +1,103 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
+
+#include <cstdint>
+#include <cstdlib>
+
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+
+namespace mediapipe::face_geometry {
+namespace {
+
+bool HasVertexComponentVertexPT(VertexComponent vertex_component) {
+  switch (vertex_component) {
+    case VertexComponent::POSITION:
+    case VertexComponent::TEX_COORD:
+      return true;
+
+    default:
+      return false;
+  }
+}
+
+uint32_t GetVertexComponentSizeVertexPT(VertexComponent vertex_component) {
+  switch (vertex_component) {
+    case VertexComponent::POSITION:
+      return 3;
+    case VertexComponent::TEX_COORD:
+      return 2;
+  }
+}
+
+uint32_t GetVertexComponentOffsetVertexPT(VertexComponent vertex_component) {
+  switch (vertex_component) {
+    case VertexComponent::POSITION:
+      return 0;
+    case VertexComponent::TEX_COORD:
+      return GetVertexComponentSizeVertexPT(VertexComponent::POSITION);
+  }
+}
+
+}  // namespace
+
+std::size_t GetVertexSize(Mesh3d::VertexType vertex_type) {
+  switch (vertex_type) {
+    case Mesh3d::VERTEX_PT:
+      return GetVertexComponentSizeVertexPT(VertexComponent::POSITION) +
+             GetVertexComponentSizeVertexPT(VertexComponent::TEX_COORD);
+  }
+}
+
+std::size_t GetPrimitiveSize(Mesh3d::PrimitiveType primitive_type) {
+  switch (primitive_type) {
+    case Mesh3d::TRIANGLE:
+      return 3;
+  }
+}
+
+bool HasVertexComponent(Mesh3d::VertexType vertex_type,
+                        VertexComponent vertex_component) {
+  switch (vertex_type) {
+    case Mesh3d::VERTEX_PT:
+      return HasVertexComponentVertexPT(vertex_component);
+  }
+}
+
+absl::StatusOr<uint32_t> GetVertexComponentOffset(
+    Mesh3d::VertexType vertex_type, VertexComponent vertex_component) {
+  RET_CHECK(HasVertexComponentVertexPT(vertex_component))
+      << "A given vertex type doesn't have the requested component!";
+
+  switch (vertex_type) {
+    case Mesh3d::VERTEX_PT:
+      return GetVertexComponentOffsetVertexPT(vertex_component);
+  }
+}
+
+absl::StatusOr<uint32_t> GetVertexComponentSize(
+    Mesh3d::VertexType vertex_type, VertexComponent vertex_component) {
+  RET_CHECK(HasVertexComponentVertexPT(vertex_component))
+      << "A given vertex type doesn't have the requested component!";
+
+  switch (vertex_type) {
+    case Mesh3d::VERTEX_PT:
+      return GetVertexComponentSizeVertexPT(vertex_component);
+  }
+}
+
+}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/mesh_3d_utils.h b/mediapipe/modules/face_geometry/libs/mesh_3d_utils.h
new file mode 100644
index 0000000..a320aae
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/mesh_3d_utils.h
@@ -0,0 +1,51 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_MESH_3D_UTILS_H_
+#define MEDIAPIPE_FACE_GEOMETRY_LIBS_MESH_3D_UTILS_H_
+
+#include <cstdint>
+#include <cstdlib>
+
+#include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+
+namespace mediapipe::face_geometry {
+
+enum class VertexComponent { POSITION, TEX_COORD };
+
+std::size_t GetVertexSize(Mesh3d::VertexType vertex_type);
+
+std::size_t GetPrimitiveSize(Mesh3d::PrimitiveType primitive_type);
+
+bool HasVertexComponent(Mesh3d::VertexType vertex_type,
+                        VertexComponent vertex_component);
+
+// Computes the vertex component offset.
+//
+// Returns an error status if a given vertex type doesn't have the requested
+// component.
+absl::StatusOr<uint32_t> GetVertexComponentOffset(
+    Mesh3d::VertexType vertex_type, VertexComponent vertex_component);
+
+// Computes the vertex component size.
+//
+// Returns an error status if a given vertex type doesn't have the requested
+// component.
+absl::StatusOr<uint32_t> GetVertexComponentSize(
+    Mesh3d::VertexType vertex_type, VertexComponent vertex_component);
+
+}  // namespace mediapipe::face_geometry
+
+#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_MESH_3D_UTILS_H_
diff --git a/mediapipe/modules/face_geometry/libs/procrustes_solver.cc b/mediapipe/modules/face_geometry/libs/procrustes_solver.cc
new file mode 100644
index 0000000..2ffae0e
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/procrustes_solver.cc
@@ -0,0 +1,266 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/face_geometry/libs/procrustes_solver.h"
+
+#include <cmath>
+#include <memory>
+
+#include "Eigen/Dense"
+#include "absl/memory/memory.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_macros.h"
+#include "mediapipe/framework/port/statusor.h"
+
+namespace mediapipe {
+namespace face_geometry {
+namespace {
+
+class FloatPrecisionProcrustesSolver : public ProcrustesSolver {
+ public:
+  FloatPrecisionProcrustesSolver() = default;
+
+  absl::Status SolveWeightedOrthogonalProblem(
+      const Eigen::Matrix3Xf& source_points,  //
+      const Eigen::Matrix3Xf& target_points,  //
+      const Eigen::VectorXf& point_weights,
+      Eigen::Matrix4f& transform_mat) const override {
+    // Validate inputs.
+    MP_RETURN_IF_ERROR(ValidateInputPoints(source_points, target_points))
+        << "Failed to validate weighted orthogonal problem input points!";
+    MP_RETURN_IF_ERROR(
+        ValidatePointWeights(source_points.cols(), point_weights))
+        << "Failed to validate weighted orthogonal problem point weights!";
+
+    // Extract square root from the point weights.
+    Eigen::VectorXf sqrt_weights = ExtractSquareRoot(point_weights);
+
+    // Try to solve the WEOP problem.
+    MP_RETURN_IF_ERROR(InternalSolveWeightedOrthogonalProblem(
+        source_points, target_points, sqrt_weights, transform_mat))
+        << "Failed to solve the WEOP problem!";
+
+    return absl::OkStatus();
+  }
+
+ private:
+  static constexpr float kAbsoluteErrorEps = 1e-9f;
+
+  static absl::Status ValidateInputPoints(
+      const Eigen::Matrix3Xf& source_points,
+      const Eigen::Matrix3Xf& target_points) {
+    RET_CHECK_GT(source_points.cols(), 0)
+        << "The number of source points must be positive!";
+
+    RET_CHECK_EQ(source_points.cols(), target_points.cols())
+        << "The number of source and target points must be equal!";
+
+    return absl::OkStatus();
+  }
+
+  static absl::Status ValidatePointWeights(
+      int num_points, const Eigen::VectorXf& point_weights) {
+    RET_CHECK_GT(point_weights.size(), 0)
+        << "The number of point weights must be positive!";
+
+    RET_CHECK_EQ(point_weights.size(), num_points)
+        << "The number of points and point weights must be equal!";
+
+    float total_weight = 0.f;
+    for (int i = 0; i < num_points; ++i) {
+      RET_CHECK_GE(point_weights(i), 0.f)
+          << "Each point weight must be non-negative!";
+
+      total_weight += point_weights(i);
+    }
+
+    RET_CHECK_GT(total_weight, kAbsoluteErrorEps)
+        << "The total point weight is too small!";
+
+    return absl::OkStatus();
+  }
+
+  static Eigen::VectorXf ExtractSquareRoot(
+      const Eigen::VectorXf& point_weights) {
+    Eigen::VectorXf sqrt_weights(point_weights);
+    for (int i = 0; i < sqrt_weights.size(); ++i) {
+      sqrt_weights(i) = std::sqrt(sqrt_weights(i));
+    }
+
+    return sqrt_weights;
+  }
+
+  // Combines a 3x3 rotation-and-scale matrix and a 3x1 translation vector into
+  // a single 4x4 transformation matrix.
+  static Eigen::Matrix4f CombineTransformMatrix(const Eigen::Matrix3f& r_and_s,
+                                                const Eigen::Vector3f& t) {
+    Eigen::Matrix4f result = Eigen::Matrix4f::Identity();
+    result.leftCols(3).topRows(3) = r_and_s;
+    result.col(3).topRows(3) = t;
+
+    return result;
+  }
+
+  // The weighted problem is thoroughly addressed in Section 2.4 of:
+  // D. Akca, Generalized Procrustes analysis and its applications
+  // in photogrammetry, 2003, https://doi.org/10.3929/ethz-a-004656648
+  //
+  // Notable differences in the code presented here are:
+  //
+  //   * In the paper, the weights matrix W_p is Cholesky-decomposed as Q^T Q.
+  //     Our W_p is diagonal (equal to diag(sqrt_weights^2)),
+  //     so we can just set Q = diag(sqrt_weights) instead.
+  //
+  //   * In the paper, the problem is presented as
+  //     (for W_k = I and W_p = tranposed(Q) Q):
+  //     || Q (c A T + j tranposed(t) - B) || -> min.
+  //
+  //     We reformulate it as an equivalent minimization of the transpose's
+  //     norm:
+  //     || (c tranposed(T) tranposed(A) - tranposed(B)) tranposed(Q) || -> min,
+  //     where tranposed(A) and tranposed(B) are the source and the target point
+  //     clouds, respectively, c tranposed(T) is the rotation+scaling R sought
+  //     for, and Q is diag(sqrt_weights).
+  //
+  //     Most of the derivations are therefore transposed.
+  //
+  // Note: the output `transform_mat` argument is used instead of `StatusOr<>`
+  // return type in order to avoid Eigen memory alignment issues. Details:
+  // https://eigen.tuxfamily.org/dox/group__TopicStructHavingEigenMembers.html
+  static absl::Status InternalSolveWeightedOrthogonalProblem(
+      const Eigen::Matrix3Xf& sources, const Eigen::Matrix3Xf& targets,
+      const Eigen::VectorXf& sqrt_weights, Eigen::Matrix4f& transform_mat) {
+    // tranposed(A_w).
+    Eigen::Matrix3Xf weighted_sources =
+        sources.array().rowwise() * sqrt_weights.array().transpose();
+    // tranposed(B_w).
+    Eigen::Matrix3Xf weighted_targets =
+        targets.array().rowwise() * sqrt_weights.array().transpose();
+
+    // w = tranposed(j_w) j_w.
+    float total_weight = sqrt_weights.cwiseProduct(sqrt_weights).sum();
+
+    // Let C = (j_w tranposed(j_w)) / (tranposed(j_w) j_w).
+    // Note that C = tranposed(C), hence (I - C) = tranposed(I - C).
+    //
+    // tranposed(A_w) C = tranposed(A_w) j_w tranposed(j_w) / w =
+    // (tranposed(A_w) j_w) tranposed(j_w) / w = c_w tranposed(j_w),
+    //
+    // where c_w = tranposed(A_w) j_w / w is a k x 1 vector calculated here:
+    Eigen::Matrix3Xf twice_weighted_sources =
+        weighted_sources.array().rowwise() * sqrt_weights.array().transpose();
+    Eigen::Vector3f source_center_of_mass =
+        twice_weighted_sources.rowwise().sum() / total_weight;
+    // tranposed((I - C) A_w) = tranposed(A_w) (I - C) =
+    // tranposed(A_w) - tranposed(A_w) C = tranposed(A_w) - c_w tranposed(j_w).
+    Eigen::Matrix3Xf centered_weighted_sources =
+        weighted_sources - source_center_of_mass * sqrt_weights.transpose();
+
+    Eigen::Matrix3f rotation;
+    MP_RETURN_IF_ERROR(ComputeOptimalRotation(
+        weighted_targets * centered_weighted_sources.transpose(), rotation))
+        << "Failed to compute the optimal rotation!";
+    ASSIGN_OR_RETURN(
+        float scale,
+        ComputeOptimalScale(centered_weighted_sources, weighted_sources,
+                            weighted_targets, rotation),
+        _ << "Failed to compute the optimal scale!");
+
+    // R = c tranposed(T).
+    Eigen::Matrix3f rotation_and_scale = scale * rotation;
+
+    // Compute optimal translation for the weighted problem.
+
+    // tranposed(B_w - c A_w T) = tranposed(B_w) - R tranposed(A_w) in (54).
+    const auto pointwise_diffs =
+        weighted_targets - rotation_and_scale * weighted_sources;
+    // Multiplication by j_w is a respectively weighted column sum.
+    // (54) from the paper.
+    const auto weighted_pointwise_diffs =
+        pointwise_diffs.array().rowwise() * sqrt_weights.array().transpose();
+    Eigen::Vector3f translation =
+        weighted_pointwise_diffs.rowwise().sum() / total_weight;
+
+    transform_mat = CombineTransformMatrix(rotation_and_scale, translation);
+
+    return absl::OkStatus();
+  }
+
+  // `design_matrix` is a transposed LHS of (51) in the paper.
+  //
+  // Note: the output `rotation` argument is used instead of `StatusOr<>`
+  // return type in order to avoid Eigen memory alignment issues. Details:
+  // https://eigen.tuxfamily.org/dox/group__TopicStructHavingEigenMembers.html
+  static absl::Status ComputeOptimalRotation(
+      const Eigen::Matrix3f& design_matrix, Eigen::Matrix3f& rotation) {
+    RET_CHECK_GT(design_matrix.norm(), kAbsoluteErrorEps)
+        << "Design matrix norm is too small!";
+
+    Eigen::JacobiSVD<Eigen::Matrix3f> svd(
+        design_matrix, Eigen::ComputeFullU | Eigen::ComputeFullV);
+
+    Eigen::Matrix3f postrotation = svd.matrixU();
+    Eigen::Matrix3f prerotation = svd.matrixV().transpose();
+
+    // Disallow reflection by ensuring that det(`rotation`) = +1 (and not -1),
+    // see "4.6 Constrained orthogonal Procrustes problems"
+    // in the Gower & Dijksterhuis's book "Procrustes Analysis".
+    // We flip the sign of the least singular value along with a column in W.
+    //
+    // Note that now the sum of singular values doesn't work for scale
+    // estimation due to this sign flip.
+    if (postrotation.determinant() * prerotation.determinant() <
+        static_cast<float>(0)) {
+      postrotation.col(2) *= static_cast<float>(-1);
+    }
+
+    // Transposed (52) from the paper.
+    rotation = postrotation * prerotation;
+    return absl::OkStatus();
+  }
+
+  static absl::StatusOr<float> ComputeOptimalScale(
+      const Eigen::Matrix3Xf& centered_weighted_sources,
+      const Eigen::Matrix3Xf& weighted_sources,
+      const Eigen::Matrix3Xf& weighted_targets,
+      const Eigen::Matrix3f& rotation) {
+    // tranposed(T) tranposed(A_w) (I - C).
+    const auto rotated_centered_weighted_sources =
+        rotation * centered_weighted_sources;
+    // Use the identity trace(A B) = sum(A * B^T)
+    // to avoid building large intermediate matrices (* is Hadamard product).
+    // (53) from the paper.
+    float numerator =
+        rotated_centered_weighted_sources.cwiseProduct(weighted_targets).sum();
+    float denominator =
+        centered_weighted_sources.cwiseProduct(weighted_sources).sum();
+
+    RET_CHECK_GT(denominator, kAbsoluteErrorEps)
+        << "Scale expression denominator is too small!";
+    RET_CHECK_GT(numerator / denominator, kAbsoluteErrorEps)
+        << "Scale is too small!";
+
+    return numerator / denominator;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<ProcrustesSolver> CreateFloatPrecisionProcrustesSolver() {
+  return absl::make_unique<FloatPrecisionProcrustesSolver>();
+}
+
+}  // namespace face_geometry
+}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/libs/procrustes_solver.h b/mediapipe/modules/face_geometry/libs/procrustes_solver.h
new file mode 100644
index 0000000..c34b8f6
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/procrustes_solver.h
@@ -0,0 +1,70 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_PROCRUSTES_SOLVER_H_
+#define MEDIAPIPE_FACE_GEOMETRY_LIBS_PROCRUSTES_SOLVER_H_
+
+#include <memory>
+
+#include "Eigen/Dense"
+#include "mediapipe/framework/port/status.h"
+
+namespace mediapipe::face_geometry {
+
+// Encapsulates a stateless solver for the Weighted Extended Orthogonal
+// Procrustes (WEOP) Problem, as defined in Section 2.4 of
+// https://doi.org/10.3929/ethz-a-004656648.
+//
+// Given the source and the target point clouds, the algorithm estimates
+// a 4x4 transformation matrix featuring the following semantic components:
+//
+//   * Uniform scale
+//   * Rotation
+//   * Translation
+//
+// The matrix maps the source point cloud into the target point cloud minimizing
+// the Mean Squared Error.
+class ProcrustesSolver {
+ public:
+  virtual ~ProcrustesSolver() = default;
+
+  // Solves the Weighted Extended Orthogonal Procrustes (WEOP) Problem.
+  //
+  // All `source_points`, `target_points` and `point_weights` must define the
+  // same number of points. Elements of `point_weights` must be non-negative.
+  //
+  // A too small diameter of either of the point clouds will likely lead to
+  // numerical instabilities and failure to estimate the transformation.
+  //
+  // A too small point cloud total weight will likely lead to numerical
+  // instabilities and failure to estimate the transformation too.
+  //
+  // Small point coordinate deviation for either of the point cloud will likely
+  // result in a failure as it will make the solution very unstable if possible.
+  //
+  // Note: the output `transform_mat` argument is used instead of `StatusOr<>`
+  // return type in order to avoid Eigen memory alignment issues. Details:
+  // https://eigen.tuxfamily.org/dox/group__TopicStructHavingEigenMembers.html
+  virtual absl::Status SolveWeightedOrthogonalProblem(
+      const Eigen::Matrix3Xf& source_points,  //
+      const Eigen::Matrix3Xf& target_points,  //
+      const Eigen::VectorXf& point_weights,   //
+      Eigen::Matrix4f& transform_mat) const = 0;
+};
+
+std::unique_ptr<ProcrustesSolver> CreateFloatPrecisionProcrustesSolver();
+
+}  // namespace mediapipe::face_geometry
+
+#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_PROCRUSTES_SOLVER_H_
diff --git a/mediapipe/modules/face_geometry/libs/validation_utils.cc b/mediapipe/modules/face_geometry/libs/validation_utils.cc
new file mode 100644
index 0000000..eb4fd08
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/validation_utils.cc
@@ -0,0 +1,126 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
+
+#include <cstdint>
+#include <cstdlib>
+
+#include "mediapipe/framework/formats/matrix_data.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_macros.h"
+#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+
+namespace mediapipe::face_geometry {
+
+absl::Status ValidatePerspectiveCamera(
+    const PerspectiveCamera& perspective_camera) {
+  static constexpr float kAbsoluteErrorEps = 1e-9f;
+
+  RET_CHECK_GT(perspective_camera.near(), kAbsoluteErrorEps)
+      << "Near Z must be greater than 0 with a margin of 10^{-9}!";
+
+  RET_CHECK_GT(perspective_camera.far(),
+               perspective_camera.near() + kAbsoluteErrorEps)
+      << "Far Z must be greater than Near Z with a margin of 10^{-9}!";
+
+  RET_CHECK_GT(perspective_camera.vertical_fov_degrees(), kAbsoluteErrorEps)
+      << "Vertical FOV must be positive with a margin of 10^{-9}!";
+
+  RET_CHECK_LT(perspective_camera.vertical_fov_degrees() + kAbsoluteErrorEps,
+               180.f)
+      << "Vertical FOV must be less than 180 degrees with a margin of 10^{-9}";
+
+  return absl::OkStatus();
+}
+
+absl::Status ValidateEnvironment(const Environment& environment) {
+  MP_RETURN_IF_ERROR(
+      ValidatePerspectiveCamera(environment.perspective_camera()))
+      << "Invalid perspective camera!";
+
+  return absl::OkStatus();
+}
+
+absl::Status ValidateMesh3d(const Mesh3d& mesh_3d) {
+  const std::size_t vertex_size = GetVertexSize(mesh_3d.vertex_type());
+  const std::size_t primitive_type = GetPrimitiveSize(mesh_3d.primitive_type());
+
+  RET_CHECK_EQ(mesh_3d.vertex_buffer_size() % vertex_size, 0)
+      << "Vertex buffer size must a multiple of the vertex size!";
+
+  RET_CHECK_EQ(mesh_3d.index_buffer_size() % primitive_type, 0)
+      << "Index buffer size must a multiple of the primitive size!";
+
+  const int num_vertices = mesh_3d.vertex_buffer_size() / vertex_size;
+  for (uint32_t idx : mesh_3d.index_buffer()) {
+    RET_CHECK_LT(idx, num_vertices)
+        << "All mesh indices must refer to an existing vertex!";
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status ValidateFaceGeometry(const FaceGeometry& face_geometry) {
+  MP_RETURN_IF_ERROR(ValidateMesh3d(face_geometry.mesh())) << "Invalid mesh!";
+
+  static constexpr char kInvalid4x4MatrixMessage[] =
+      "Pose transformation matrix must be a 4x4 matrix!";
+
+  const MatrixData& pose_transform_matrix =
+      face_geometry.pose_transform_matrix();
+  RET_CHECK_EQ(pose_transform_matrix.rows(), 4) << kInvalid4x4MatrixMessage;
+  RET_CHECK_EQ(pose_transform_matrix.rows(), 4) << kInvalid4x4MatrixMessage;
+  RET_CHECK_EQ(pose_transform_matrix.packed_data_size(), 16)
+      << kInvalid4x4MatrixMessage;
+
+  return absl::OkStatus();
+}
+
+absl::Status ValidateGeometryPipelineMetadata(
+    const GeometryPipelineMetadata& metadata) {
+  MP_RETURN_IF_ERROR(ValidateMesh3d(metadata.canonical_mesh()))
+      << "Invalid canonical mesh!";
+
+  RET_CHECK_GT(metadata.procrustes_landmark_basis_size(), 0)
+
+      << "Procrustes landmark basis must be non-empty!";
+
+  const int num_vertices =
+      metadata.canonical_mesh().vertex_buffer_size() /
+      GetVertexSize(metadata.canonical_mesh().vertex_type());
+  for (const WeightedLandmarkRef& wlr : metadata.procrustes_landmark_basis()) {
+    RET_CHECK_LT(wlr.landmark_id(), num_vertices)
+        << "All Procrustes basis indices must refer to an existing canonical "
+           "mesh vertex!";
+
+    RET_CHECK_GE(wlr.weight(), 0.f)
+        << "All Procrustes basis landmarks must have a non-negative weight!";
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status ValidateFrameDimensions(int frame_width, int frame_height) {
+  RET_CHECK_GT(frame_width, 0) << "Frame width must be positive!";
+  RET_CHECK_GT(frame_height, 0) << "Frame height must be positive!";
+
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/validation_utils.h b/mediapipe/modules/face_geometry/libs/validation_utils.h
new file mode 100644
index 0000000..c0a7e08
--- /dev/null
+++ b/mediapipe/modules/face_geometry/libs/validation_utils.h
@@ -0,0 +1,70 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_VALIDATION_UTILS_H_
+#define MEDIAPIPE_FACE_GEOMETRY_LIBS_VALIDATION_UTILS_H_
+
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
+#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
+#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
+#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
+
+namespace mediapipe::face_geometry {
+
+// Validates `perspective_camera`.
+//
+// Near Z must be greater than 0 with a margin of `1e-9`.
+// Far Z must be greater than Near Z with a margin of `1e-9`.
+// Vertical FOV must be in range (0, 180) with a margin of `1e-9` on the range
+// edges.
+absl::Status ValidatePerspectiveCamera(
+    const PerspectiveCamera& perspective_camera);
+
+// Validates `environment`.
+//
+// Environment's perspective camera must be valid.
+absl::Status ValidateEnvironment(const Environment& environment);
+
+// Validates `mesh_3d`.
+//
+// Mesh vertex buffer size must a multiple of the vertex size.
+// Mesh index buffer size must a multiple of the primitive size.
+// All mesh indices must reference an existing mesh vertex.
+absl::Status ValidateMesh3d(const Mesh3d& mesh_3d);
+
+// Validates `face_geometry`.
+//
+// Face mesh must be valid.
+// Face pose transformation matrix must be a 4x4 matrix.
+absl::Status ValidateFaceGeometry(const FaceGeometry& face_geometry);
+
+// Validates `metadata`.
+//
+// Canonical face mesh must be valid.
+// Procrustes landmark basis must be non-empty.
+// All Procrustes basis indices must reference an existing canonical mesh
+// vertex.
+// All Procrustes basis landmarks must have a non-negative weight.
+absl::Status ValidateGeometryPipelineMetadata(
+    const GeometryPipelineMetadata& metadata);
+
+// Validates frame dimensions.
+//
+// Both frame width and frame height must be positive.
+absl::Status ValidateFrameDimensions(int frame_width, int frame_height);
+
+}  // namespace mediapipe::face_geometry
+
+#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_VALIDATION_UTILS_H_
diff --git a/mediapipe/modules/face_geometry/protos/BUILD b/mediapipe/modules/face_geometry/protos/BUILD
new file mode 100644
index 0000000..48b7b66
--- /dev/null
+++ b/mediapipe/modules/face_geometry/protos/BUILD
@@ -0,0 +1,46 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_proto_library(
+    name = "environment_proto",
+    srcs = ["environment.proto"],
+)
+
+mediapipe_proto_library(
+    name = "face_geometry_proto",
+    srcs = ["face_geometry.proto"],
+    deps = [
+        ":mesh_3d_proto",
+        "//mediapipe/framework/formats:matrix_data_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "geometry_pipeline_metadata_proto",
+    srcs = ["geometry_pipeline_metadata.proto"],
+    deps = [
+        ":mesh_3d_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "mesh_3d_proto",
+    srcs = ["mesh_3d.proto"],
+)
diff --git a/mediapipe/modules/face_geometry/protos/environment.proto b/mediapipe/modules/face_geometry/protos/environment.proto
new file mode 100644
index 0000000..cca3f29
--- /dev/null
+++ b/mediapipe/modules/face_geometry/protos/environment.proto
@@ -0,0 +1,84 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe.face_geometry;
+
+option java_package = "com.google.mediapipe.modules.facegeometry";
+option java_outer_classname = "EnvironmentProto";
+
+// Defines the (0, 0) origin point location of the environment.
+//
+// The variation in the origin point location can be traced back to the memory
+// layout of the camera video frame buffers.
+//
+// Usually, the memory layout for most CPU (and also some GPU) camera video
+// frame buffers results in having the (0, 0) origin point located in the
+// Top Left corner.
+//
+// On the contrary, the memory layout for most GPU camera video frame buffers
+// results in having the (0, 0) origin point located in the Bottom Left corner.
+//
+// Let's consider the following example:
+//
+// (A) ---------------+
+//               ___  |
+//  |     (1)    | |  |
+//  |     / \    | |  |
+//  |    |---|===|-|  |
+//  |    |---|   | |  |
+//  |   /     \  | |  |
+//  |  |       | | |  |
+//  |  |  (2)  |=| |  |
+//  |  |       | | |  |
+//  |  |_______| |_|  |
+//  |   |@| |@|  | |  |
+//  | ___________|_|_ |
+//                    |
+// (B) ---------------+
+//
+// On this example, (1) and (2) have the same X coordinate regardless of the
+// origin point location. However, having the origin point located at (A)
+// (Top Left corner) results in (1) having a smaller Y coordinate if compared to
+// (2). Similarly, having the origin point located at (B) (Bottom Left corner)
+// results in (1) having a greater Y coordinate if compared to (2).
+//
+// Providing the correct origin point location for your environment and making
+// sure all the input landmarks are in-sync with this location is crucial
+// for receiving the correct output face geometry and visual renders.
+enum OriginPointLocation {
+  BOTTOM_LEFT_CORNER = 1;
+  TOP_LEFT_CORNER = 2;
+}
+
+// The perspective camera is defined through its vertical FOV angle and the
+// Z-clipping planes. The aspect ratio is a runtime variable for the face
+// geometry module and should be provided alongside the face landmarks in order
+// to estimate the face geometry on a given frame.
+//
+// More info on Perspective Cameras:
+// http://www.songho.ca/opengl/gl_projectionmatrix.html#perspective
+message PerspectiveCamera {
+  // `0 < vertical_fov_degrees < 180`.
+  optional float vertical_fov_degrees = 1;
+  // `0 < near < far`.
+  optional float near = 2;
+  optional float far = 3;
+}
+
+message Environment {
+  optional OriginPointLocation origin_point_location = 1;
+  optional PerspectiveCamera perspective_camera = 2;
+}
diff --git a/mediapipe/modules/face_geometry/protos/face_geometry.proto b/mediapipe/modules/face_geometry/protos/face_geometry.proto
new file mode 100644
index 0000000..b91a7d7
--- /dev/null
+++ b/mediapipe/modules/face_geometry/protos/face_geometry.proto
@@ -0,0 +1,60 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe.face_geometry;
+
+import "mediapipe/framework/formats/matrix_data.proto";
+import "mediapipe/modules/face_geometry/protos/mesh_3d.proto";
+
+option java_package = "com.google.mediapipe.modules.facegeometry";
+option java_outer_classname = "FaceGeometryProto";
+
+// Defines the face geometry pipeline estimation result format.
+message FaceGeometry {
+  // Defines a mesh surface for a face. The face mesh vertex IDs are the same as
+  // the face landmark IDs.
+  //
+  // XYZ coordinates exist in the right-handed Metric 3D space configured by an
+  // environment. UV coodinates are taken from the canonical face mesh model.
+  //
+  // XY coordinates are guaranteed to match the screen positions of
+  // the input face landmarks after (1) being multiplied by the face pose
+  // transformation matrix and then (2) being projected with a perspective
+  // camera matrix of the same environment.
+  //
+  // NOTE: the triangular topology of the face mesh is only useful when derived
+  //       from the 468 face landmarks, not from the 6 face detection landmarks
+  //       (keypoints). The former don't cover the entire face and this mesh is
+  //       defined here only to comply with the API. It should be considered as
+  //       a placeholder and/or for debugging purposes.
+  //
+  //       Use the face geometry derived from the face detection landmarks
+  //       (keypoints) for the face pose transformation matrix, not the mesh.
+  optional Mesh3d mesh = 1;
+
+  // Defines a face pose transformation matrix, which provides mapping from
+  // the static canonical face model to the runtime face. Tries to distinguish
+  // a head pose change from a facial expression change and to only reflect the
+  // former.
+  //
+  // Is a 4x4 matrix and contains only the following components:
+  //   * Uniform scale
+  //   * Rotation
+  //   * Translation
+  //
+  // The last row is guaranteed to be `[0 0 0 1]`.
+  optional MatrixData pose_transform_matrix = 2;
+}
diff --git a/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto b/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto
new file mode 100644
index 0000000..dac0e25
--- /dev/null
+++ b/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto
@@ -0,0 +1,63 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe.face_geometry;
+
+import "mediapipe/modules/face_geometry/protos/mesh_3d.proto";
+
+option java_package = "com.google.mediapipe.modules.facegeometry";
+option java_outer_classname = "GeometryPipelineMetadataProto";
+
+enum InputSource {
+  DEFAULT = 0;  // FACE_LANDMARK_PIPELINE
+  FACE_LANDMARK_PIPELINE = 1;
+  FACE_DETECTION_PIPELINE = 2;
+}
+
+message WeightedLandmarkRef {
+  // Defines the landmark ID. References an existing face landmark ID.
+  optional uint32 landmark_id = 1;
+  // Defines the landmark weight. The larger the weight the more influence this
+  // landmark has in the basis.
+  //
+  // Is positive.
+  optional float weight = 2;
+}
+
+// Next field ID: 4
+message GeometryPipelineMetadata {
+  // Defines the source of the input landmarks to let the underlying geometry
+  // pipeline to adjust in order to produce the best results.
+  //
+  // Face landmark pipeline is expected to produce 3D landmarks with relative Z
+  // coordinate, which is scaled as the X coordinate assuming the weak
+  // perspective projection camera model.
+  //
+  // Face landmark pipeline is expected to produce 2D landmarks with Z
+  // coordinate being equal to 0.
+  optional InputSource input_source = 3;
+  // Defines a mesh surface for a canonical face. The canonical face mesh vertex
+  // IDs are the same as the face landmark IDs.
+  //
+  // XYZ coordinates are defined in centimeter units.
+  optional Mesh3d canonical_mesh = 1;
+  // Defines a weighted landmark basis for running the Procrustes solver
+  // algorithm inside the geometry pipeline.
+  //
+  // A good basis sets face landmark weights in way to distinguish a head pose
+  // change from a facial expression change and to only respond to the former.
+  repeated WeightedLandmarkRef procrustes_landmark_basis = 2;
+}
diff --git a/mediapipe/modules/face_geometry/protos/mesh_3d.proto b/mediapipe/modules/face_geometry/protos/mesh_3d.proto
new file mode 100644
index 0000000..4db45c1
--- /dev/null
+++ b/mediapipe/modules/face_geometry/protos/mesh_3d.proto
@@ -0,0 +1,41 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe.face_geometry;
+
+option java_package = "com.google.mediapipe.modules.facegeometry";
+option java_outer_classname = "Mesh3dProto";
+
+message Mesh3d {
+  enum VertexType {
+    // Is defined by 5 coordinates: Position (XYZ) + Texture coordinate (UV).
+    VERTEX_PT = 0;
+  }
+
+  enum PrimitiveType {
+    // Is defined by 3 indices: triangle vertex IDs.
+    TRIANGLE = 0;
+  }
+
+  optional VertexType vertex_type = 1;
+  optional PrimitiveType primitive_type = 2;
+  // Vertex buffer size is a multiple of the vertex size (e.g., 5 for
+  // VERTEX_PT).
+  repeated float vertex_buffer = 3;
+  // Index buffer size is a multiple of the primitive size (e.g., 3 for
+  // TRIANGLE).
+  repeated uint32 index_buffer = 4;
+}
diff --git a/mediapipe/modules/face_landmark/BUILD b/mediapipe/modules/face_landmark/BUILD
new file mode 100644
index 0000000..f155e46
--- /dev/null
+++ b/mediapipe/modules/face_landmark/BUILD
@@ -0,0 +1,190 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "face_landmark_cpu",
+    graph = "face_landmark_cpu.pbtxt",
+    register_as = "FaceLandmarkCpu",
+    deps = [
+        ":face_landmarks_model_loader",
+        ":tensors_to_face_landmarks",
+        ":tensors_to_face_landmarks_with_attention",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmark_gpu",
+    graph = "face_landmark_gpu.pbtxt",
+    register_as = "FaceLandmarkGpu",
+    deps = [
+        ":face_landmarks_model_loader",
+        ":tensors_to_face_landmarks",
+        ":tensors_to_face_landmarks_with_attention",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmark_front_cpu",
+    graph = "face_landmark_front_cpu.pbtxt",
+    register_as = "FaceLandmarkFrontCpu",
+    deps = [
+        ":face_detection_front_detection_to_roi",
+        ":face_landmark_cpu",
+        ":face_landmark_landmarks_to_roi",
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:association_norm_rect_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmark_front_gpu",
+    graph = "face_landmark_front_gpu.pbtxt",
+    register_as = "FaceLandmarkFrontGpu",
+    deps = [
+        ":face_detection_front_detection_to_roi",
+        ":face_landmark_gpu",
+        ":face_landmark_landmarks_to_roi",
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:association_norm_rect_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmark_front_cpu_image",
+    graph = "face_landmark_front_cpu_image.pbtxt",
+    register_as = "FaceLandmarkFrontCpuImage",
+    deps = [
+        ":face_landmark_front_cpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmark_front_gpu_image",
+    graph = "face_landmark_front_gpu_image.pbtxt",
+    register_as = "FaceLandmarkFrontGpuImage",
+    deps = [
+        ":face_landmark_front_gpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "face_landmark.tflite",
+        "face_landmark_with_attention.tflite",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_front_detection_to_roi",
+    graph = "face_detection_front_detection_to_roi.pbtxt",
+    register_as = "FaceDetectionFrontDetectionToRoi",
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmark_landmarks_to_roi",
+    graph = "face_landmark_landmarks_to_roi.pbtxt",
+    register_as = "FaceLandmarkLandmarksToRoi",
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmarks_model_loader",
+    graph = "face_landmarks_model_loader.pbtxt",
+    register_as = "FaceLandmarksModelLoader",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/tflite:tflite_model_calculator",
+        "//mediapipe/calculators/util:local_file_contents_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "tensors_to_face_landmarks",
+    graph = "tensors_to_face_landmarks.pbtxt",
+    register_as = "TensorsToFaceLandmarks",
+    deps = [
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "tensors_to_face_landmarks_with_attention",
+    graph = "tensors_to_face_landmarks_with_attention.pbtxt",
+    register_as = "TensorsToFaceLandmarksWithAttention",
+    deps = [
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:landmarks_refinement_calculator",
+    ],
+)
diff --git a/mediapipe/modules/face_landmark/README.md b/mediapipe/modules/face_landmark/README.md
new file mode 100644
index 0000000..eed21a2
--- /dev/null
+++ b/mediapipe/modules/face_landmark/README.md
@@ -0,0 +1,9 @@
+# face_landmark
+
+Subgraphs|Details
+:--- | :---
+[`FaceLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt)| Detects landmarks on a single face. (CPU input, and inference is executed on CPU.)
+[`FaceLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt)| Detects landmarks on a single face. (GPU input, and inference is executed on GPU)
+[`FaceLandmarkFrontCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt)| Detects and tracks landmarks on multiple faces. (CPU input, and inference is executed on CPU)
+[`FaceLandmarkFrontGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt)| Detects and tracks landmarks on multiple faces. (GPU input, and inference is executed on GPU.)
+
diff --git a/mediapipe/modules/face_landmark/face_detection_front_detection_to_roi.pbtxt b/mediapipe/modules/face_landmark/face_detection_front_detection_to_roi.pbtxt
new file mode 100644
index 0000000..acc9476
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_detection_front_detection_to_roi.pbtxt
@@ -0,0 +1,47 @@
+# MediaPipe graph to calculate face region of interest (ROI) from the very
+# first face detection in the vector of detections provided by
+# "FaceDetectionShortRangeCpu" or "FaceDetectionShortRangeGpu"
+#
+# NOTE: this graph is subject to change and should not be used directly.
+
+type: "FaceDetectionFrontDetectionToRoi"
+
+# Face detection. (Detection)
+input_stream: "DETECTION:detection"
+# Frame size (width and height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+# ROI according to the first detection of input detections. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts results of face detection into a rectangle (normalized by image size)
+# that encloses the face and is rotated such that the line connecting left eye
+# and right eye is aligned with the X-axis of the rectangle.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTION:detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:initial_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0  # Left eye.
+      rotation_vector_end_keypoint_index: 1  # Right eye.
+      rotation_vector_target_angle_degrees: 0
+    }
+  }
+}
+
+# Expands and shifts the rectangle that contains the face so that it's likely
+# to cover the entire face.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:initial_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.5
+      scale_y: 1.5
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark.tflite b/mediapipe/modules/face_landmark/face_landmark.tflite
new file mode 100755
index 0000000..573285d
Binary files /dev/null and b/mediapipe/modules/face_landmark/face_landmark.tflite differ
diff --git a/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt
new file mode 100644
index 0000000..4604fc7
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt
@@ -0,0 +1,184 @@
+# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
+# executed on CPU.)
+#
+# It is required that "face_landmark.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark.tflite"
+# path during execution if `with_attention` is not set or set to `false`.
+#
+# It is required that "face_landmark_with_attention.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
+# path during execution if `with_attention` is set to `true`.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceLandmarkCpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:face_roi"
+#     input_side_packet: "WITH_ATTENTION:with_attention"
+#     output_stream: "LANDMARKS:face_landmarks"
+#   }
+
+type: "FaceLandmarkCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a face is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+# Whether to run face mesh model with attention on lips and eyes. (bool)
+# Attention provides more accuracy on lips and eye regions as well as iris
+# landmarks.
+input_side_packet: "WITH_ATTENTION:with_attention"
+
+# 468 or 478 facial landmarks within the given ROI. (NormalizedLandmarkList)
+#
+# Number of landmarks depends on the WITH_ATTENTION flag. If it's `true` - then
+# there will be 478 landmarks with refined lips, eyes and irises (10 extra
+# landmarks are for irises), otherwise 468 non-refined landmarks are returned.
+#
+# NOTE: if a face is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:face_landmarks"
+
+# Transforms the input image into a 192x192 tensor.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:input_tensors"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 192
+      output_tensor_height: 192
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+    }
+  }
+}
+
+# Loads the face landmarks TF Lite model.
+node {
+  calculator: "FaceLandmarksModelLoader"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_side_packet: "MODEL:model"
+}
+
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "op_resolver"
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  input_side_packet: "MODEL:model"
+  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Splits a vector of tensors into landmark tensors and face flag tensor.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:with_attention"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "face_flag_tensor"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      contained_node: {
+        calculator: "SplitTensorVectorCalculator"
+        options: {
+          [mediapipe.SplitVectorCalculatorOptions.ext] {
+            ranges: { begin: 0 end: 1 }
+            ranges: { begin: 1 end: 2 }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "SplitTensorVectorCalculator"
+        options: {
+          [mediapipe.SplitVectorCalculatorOptions.ext] {
+            ranges: { begin: 0 end: 6 }
+            ranges: { begin: 6 end: 7 }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Converts the face-flag tensor into a float that represents the confidence
+# score of face presence.
+node {
+  calculator: "TensorsToFloatsCalculator"
+  input_stream: "TENSORS:face_flag_tensor"
+  output_stream: "FLOAT:face_presence_score"
+  options {
+    [mediapipe.TensorsToFloatsCalculatorOptions.ext] {
+      activation: SIGMOID
+    }
+  }
+}
+
+# Applies a threshold to the confidence score to determine whether a face is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:face_presence_score"
+  output_stream: "FLAG:face_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.5
+    }
+  }
+}
+
+# Drop landmarks tensors if face is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:face_presence"
+  output_stream: "ensured_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:with_attention"
+  input_stream: "TENSORS:ensured_landmark_tensors"
+  output_stream: "LANDMARKS:landmarks"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      contained_node: {
+        calculator: "TensorsToFaceLandmarks"
+      }
+      contained_node: {
+        calculator: "TensorsToFaceLandmarksWithAttention"
+      }
+    }
+  }
+}
+
+# Projects the landmarks from the cropped face image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:face_landmarks"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt
new file mode 100644
index 0000000..70a57b0
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt
@@ -0,0 +1,247 @@
+# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
+# executed on CPU.) This graph tries to skip face detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# It is required that "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+# path during execution.
+#
+# It is required that "face_landmark.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark.tflite"
+# path during execution if `with_attention` is not set or set to `false`.
+#
+# It is required that "face_landmark_with_attention.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
+# path during execution if `with_attention` is set to `true`.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceLandmarkFrontCpu"
+#     input_stream: "IMAGE:image"
+#     input_side_packet: "NUM_FACES:num_faces"
+#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+#     input_side_packet: "WITH_ATTENTION:with_attention"
+#     output_stream: "LANDMARKS:multi_face_landmarks"
+#   }
+
+type: "FaceLandmarkFrontCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Max number of faces to detect/track. (int)
+input_side_packet: "NUM_FACES:num_faces"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Whether to run face mesh model with attention on lips and eyes. (bool)
+# Attention provides more accuracy on lips and eye regions as well as iris
+# landmarks.
+input_side_packet: "WITH_ATTENTION:with_attention"
+
+# Collection of detected/predicted faces, each represented as a list of 468 face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_face_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected faces. (std::vector<Detection>)
+output_stream: "DETECTIONS:face_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+# Regions of interest calculated based on face detections.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_face_rects_from_landmarks"
+  output_stream: "gated_prev_face_rects_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided num_faces.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:gated_prev_face_rects_from_landmarks"
+  input_side_packet: "num_faces"
+  output_stream: "prev_has_enough_faces"
+}
+
+# Drops the incoming image if enough faces have already been identified from the
+# previous image. Otherwise, passes the incoming image through to trigger a new
+# round of face detection.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_faces"
+  output_stream: "gated_image"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionShortRangeCpu"
+  input_stream: "IMAGE:gated_image"
+  output_stream: "DETECTIONS:all_face_detections"
+}
+
+# Makes sure there are no more detections than the provided num_faces.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "all_face_detections"
+  output_stream: "face_detections"
+  input_side_packet: "num_faces"
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:gated_image"
+  output_stream: "SIZE:gated_image_size"
+}
+
+# Outputs each element of face_detections at a fake timestamp for the rest of
+# the graph to process. Clones the image size packet for each face_detection at
+# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
+# for downstream calculators to inform them that all elements in the vector have
+# been processed.
+node {
+  calculator: "BeginLoopDetectionCalculator"
+  input_stream: "ITERABLE:face_detections"
+  input_stream: "CLONE:gated_image_size"
+  output_stream: "ITEM:face_detection"
+  output_stream: "CLONE:detections_loop_image_size"
+  output_stream: "BATCH_END:detections_loop_end_timestamp"
+}
+
+# Calculates region of interest based on face detections, so that can be used
+# to detect landmarks.
+node {
+  calculator: "FaceDetectionFrontDetectionToRoi"
+  input_stream: "DETECTION:face_detection"
+  input_stream: "IMAGE_SIZE:detections_loop_image_size"
+  output_stream: "ROI:face_rect_from_detection"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_detection"
+  input_stream: "BATCH_END:detections_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_detections"
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on face detections from the current image. This
+# calculator ensures that the output face_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "face_rects_from_detections"
+  input_stream: "gated_prev_face_rects_from_landmarks"
+  output_stream: "face_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.5
+    }
+  }
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Outputs each element of face_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_face_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:face_rects"
+  input_stream: "CLONE:0:image"
+  input_stream: "CLONE:1:image_size"
+  output_stream: "ITEM:face_rect"
+  output_stream: "CLONE:0:landmarks_loop_image"
+  output_stream: "CLONE:1:landmarks_loop_image_size"
+  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
+}
+
+# Detects face landmarks within specified region of interest of the image.
+node {
+  calculator: "FaceLandmarkCpu"
+  input_stream: "IMAGE:landmarks_loop_image"
+  input_stream: "ROI:face_rect"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:face_landmarks"
+}
+
+# Calculates region of interest based on face landmarks, so that can be reused
+# for subsequent image.
+node {
+  calculator: "FaceLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:face_landmarks"
+  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
+  output_stream: "ROI:face_rect_from_landmarks"
+}
+
+# Collects a set of landmarks for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:face_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:multi_face_landmarks"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_landmarks"
+}
+
+# Caches face rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# face rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:face_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_cpu_image.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_cpu_image.pbtxt
new file mode 100644
index 0000000..7d0c46a
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_front_cpu_image.pbtxt
@@ -0,0 +1,87 @@
+# MediaPipe graph to detect/predict face landmarks on CPU.
+
+type: "FaceLandmarkFrontCpuImage"
+
+# Input image. (Image)
+input_stream: "IMAGE:image"
+
+# Max number of faces to detect/track. (int)
+input_side_packet: "NUM_FACES:num_faces"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Whether to run face mesh model with attention on lips and eyes. (bool)
+# Attention provides more accuracy on lips and eye regions as well as iris
+# landmarks.
+input_side_packet: "WITH_ATTENTION:with_attention"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+# Collection of detected/predicted faces, each represented as a list of 468 face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_face_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected faces. (std::vector<Detection>)
+output_stream: "DETECTIONS:face_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+# Regions of interest calculated based on face detections.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:multi_face_landmarks"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Converts Image to ImageFrame for FaceLandmarkFrontCpu to consume.
+node {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "IMAGE_CPU:raw_image_frame"
+  output_stream: "SOURCE_ON_GPU:is_gpu_image"
+}
+
+# TODO: Remove the extra flipping once adopting MlImage.
+# If the source images are on gpu, flip the data vertically before sending them
+# into FaceLandmarkFrontCpu. This maybe needed because OpenGL represents images
+# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
+# general assumes the image origin is at the top-left corner.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:raw_image_frame"
+  input_stream: "FLIP_VERTICALLY:is_gpu_image"
+  output_stream: "IMAGE:image_frame"
+}
+
+node {
+  calculator: "FaceLandmarkFrontCpu"
+  input_stream: "IMAGE:image_frame"
+  input_side_packet: "NUM_FACES:num_faces"
+  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt
new file mode 100644
index 0000000..fd89565
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt
@@ -0,0 +1,247 @@
+# MediaPipe graph to detect/predict face landmarks. (GPU input, and inference is
+# executed on GPU.) This graph tries to skip face detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# It is required that "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+# path during execution.
+#
+# It is required that "face_landmark.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark.tflite"
+# path during execution if `with_attention` is not set or set to `false`.
+#
+# It is required that "face_landmark_with_attention.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
+# path during execution if `with_attention` is set to `true`.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceLandmarkFrontGpu"
+#     input_stream: "IMAGE:image"
+#     input_side_packet: "NUM_FACES:num_faces"
+#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+#     input_side_packet: "WITH_ATTENTION:with_attention"
+#     output_stream: "LANDMARKS:multi_face_landmarks"
+#   }
+
+type: "FaceLandmarkFrontGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Max number of faces to detect/track. (int)
+input_side_packet: "NUM_FACES:num_faces"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Whether to run face mesh model with attention on lips and eyes. (bool)
+# Attention provides more accuracy on lips and eye regions as well as iris
+# landmarks.
+input_side_packet: "WITH_ATTENTION:with_attention"
+
+# Collection of detected/predicted faces, each represented as a list of 468 face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_face_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected faces. (std::vector<Detection>)
+output_stream: "DETECTIONS:face_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+# Regions of interest calculated based on face detections.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_face_rects_from_landmarks"
+  output_stream: "gated_prev_face_rects_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided num_faces.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:gated_prev_face_rects_from_landmarks"
+  input_side_packet: "num_faces"
+  output_stream: "prev_has_enough_faces"
+}
+
+# Drops the incoming image if enough faces have already been identified from the
+# previous image. Otherwise, passes the incoming image through to trigger a new
+# round of face detection.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_faces"
+  output_stream: "gated_image"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionShortRangeGpu"
+  input_stream: "IMAGE:gated_image"
+  output_stream: "DETECTIONS:all_face_detections"
+}
+
+# Makes sure there are no more detections than the provided num_faces.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "all_face_detections"
+  output_stream: "face_detections"
+  input_side_packet: "num_faces"
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:gated_image"
+  output_stream: "SIZE:gated_image_size"
+}
+
+# Outputs each element of face_detections at a fake timestamp for the rest of
+# the graph to process. Clones the image size packet for each face_detection at
+# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
+# for downstream calculators to inform them that all elements in the vector have
+# been processed.
+node {
+  calculator: "BeginLoopDetectionCalculator"
+  input_stream: "ITERABLE:face_detections"
+  input_stream: "CLONE:gated_image_size"
+  output_stream: "ITEM:face_detection"
+  output_stream: "CLONE:detections_loop_image_size"
+  output_stream: "BATCH_END:detections_loop_end_timestamp"
+}
+
+# Calculates region of interest based on face detections, so that can be used
+# to detect landmarks.
+node {
+  calculator: "FaceDetectionFrontDetectionToRoi"
+  input_stream: "DETECTION:face_detection"
+  input_stream: "IMAGE_SIZE:detections_loop_image_size"
+  output_stream: "ROI:face_rect_from_detection"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_detection"
+  input_stream: "BATCH_END:detections_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_detections"
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on face detections from the current image. This
+# calculator ensures that the output face_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "face_rects_from_detections"
+  input_stream: "gated_prev_face_rects_from_landmarks"
+  output_stream: "face_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.5
+    }
+  }
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Outputs each element of face_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_face_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:face_rects"
+  input_stream: "CLONE:0:image"
+  input_stream: "CLONE:1:image_size"
+  output_stream: "ITEM:face_rect"
+  output_stream: "CLONE:0:landmarks_loop_image"
+  output_stream: "CLONE:1:landmarks_loop_image_size"
+  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
+}
+
+# Detects face landmarks within specified region of interest of the image.
+node {
+  calculator: "FaceLandmarkGpu"
+  input_stream: "IMAGE:landmarks_loop_image"
+  input_stream: "ROI:face_rect"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:face_landmarks"
+}
+
+# Calculates region of interest based on face landmarks, so that can be reused
+# for subsequent image.
+node {
+  calculator: "FaceLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:face_landmarks"
+  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
+  output_stream: "ROI:face_rect_from_landmarks"
+}
+
+# Collects a set of landmarks for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:face_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:multi_face_landmarks"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_landmarks"
+}
+
+# Caches face rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# face rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:face_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_gpu_image.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_gpu_image.pbtxt
new file mode 100644
index 0000000..31da4b8
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_front_gpu_image.pbtxt
@@ -0,0 +1,87 @@
+# MediaPipe graph to detect/predict face landmarks on GPU.
+
+type: "FaceLandmarkFrontGpuImage"
+
+# Input image. (Image)
+input_stream: "IMAGE:image"
+
+# Max number of faces to detect/track. (int)
+input_side_packet: "NUM_FACES:num_faces"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Whether to run face mesh model with attention on lips and eyes. (bool)
+# Attention provides more accuracy on lips and eye regions as well as iris
+# landmarks.
+input_side_packet: "WITH_ATTENTION:with_attention"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+# Collection of detected/predicted faces, each represented as a list of 468 face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_face_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected faces. (std::vector<Detection>)
+output_stream: "DETECTIONS:face_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+# Regions of interest calculated based on face detections.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:multi_face_landmarks"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Converts Image to GpuBuffer for FaceLandmarkFrontGpu to consume.
+node {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "IMAGE_GPU:raw_gpu_buffer"
+  output_stream: "SOURCE_ON_GPU:is_gpu_image"
+}
+
+# TODO: Remove the extra flipping once adopting MlImage.
+# If the source images are on gpu, flip the data vertically before sending them
+# into FaceLandmarkFrontGpu. This maybe needed because OpenGL represents images
+# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
+# general assumes the image origin is at the top-left corner.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:raw_gpu_buffer"
+  input_stream: "FLIP_VERTICALLY:is_gpu_image"
+  output_stream: "IMAGE_GPU:gpu_buffer"
+}
+
+node {
+  calculator: "FaceLandmarkFrontGpu"
+  input_stream: "IMAGE:gpu_buffer"
+  input_side_packet: "NUM_FACES:num_faces"
+  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_side_model_cpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_side_model_cpu.pbtxt
new file mode 100644
index 0000000..d3d26c0
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_front_side_model_cpu.pbtxt
@@ -0,0 +1,224 @@
+# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
+# executed on CPU.) This graph tries to skip face detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceLandmarkFrontSideModelCpu"
+#     input_stream: "IMAGE:image"
+#     input_side_packet: "NUM_FACES:num_faces"
+#     input_side_packet: "MODEL:0:face_detection_model"
+#     input_side_packet: "MODEL:1:face_landmark_model"
+#     output_stream: "LANDMARKS:multi_face_landmarks"
+#   }
+
+type: "FaceLandmarkFrontSideModelCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Max number of faces to detect/track. (int)
+input_side_packet: "NUM_FACES:num_faces"
+# TfLite model to detect faces.
+# (std::unique_ptr<tflite::FlatBufferModel,
+#      std::function<void(tflite::FlatBufferModel*)>>)
+# NOTE: mediapipe/modules/face_detection/face_detection_short_range.tflite
+# model only, can be passed here, otherwise - results are undefined.
+input_side_packet: "MODEL:0:face_detection_model"
+# TfLite model to detect face landmarks.
+# (std::unique_ptr<tflite::FlatBufferModel,
+#      std::function<void(tflite::FlatBufferModel*)>>)
+# NOTE: mediapipe/modules/face_landmark/face_landmark.tflite model
+# only, can be passed here, otherwise - results are undefined.
+input_side_packet: "MODEL:1:face_landmark_model"
+
+# Collection of detected/predicted faces, each represented as a list of 468 face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_face_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected faces. (std::vector<Detection>)
+output_stream: "DETECTIONS:face_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+# Regions of interest calculated based on face detections.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided num_faces.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:prev_face_rects_from_landmarks"
+  input_side_packet: "num_faces"
+  output_stream: "prev_has_enough_faces"
+}
+
+# Drops the incoming image if FaceLandmarkCpu was able to identify face presence
+# in the previous image. Otherwise, passes the incoming image through to trigger
+# a new round of face detection in FaceDetectionShortRangeCpu.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_faces"
+  output_stream: "gated_image"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionShortRangeSideModelCpu"
+  input_stream: "IMAGE:gated_image"
+  input_side_packet: "MODEL:face_detection_model"
+  output_stream: "DETECTIONS:all_face_detections"
+}
+
+# Makes sure there are no more detections than the provided num_faces.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "all_face_detections"
+  output_stream: "face_detections"
+  input_side_packet: "num_faces"
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:gated_image"
+  output_stream: "SIZE:gated_image_size"
+}
+
+# Outputs each element of face_detections at a fake timestamp for the rest of
+# the graph to process. Clones the image size packet for each face_detection at
+# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
+# for downstream calculators to inform them that all elements in the vector have
+# been processed.
+node {
+  calculator: "BeginLoopDetectionCalculator"
+  input_stream: "ITERABLE:face_detections"
+  input_stream: "CLONE:gated_image_size"
+  output_stream: "ITEM:face_detection"
+  output_stream: "CLONE:detections_loop_image_size"
+  output_stream: "BATCH_END:detections_loop_end_timestamp"
+}
+
+# Calculates region of interest based on face detections, so that can be used
+# to detect landmarks.
+node {
+  calculator: "FaceDetectionFrontDetectionToRoi"
+  input_stream: "DETECTION:face_detection"
+  input_stream: "IMAGE_SIZE:detections_loop_image_size"
+  output_stream: "ROI:face_rect_from_detection"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_detection"
+  input_stream: "BATCH_END:detections_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_detections"
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on face detections from the current image. This
+# calculator ensures that the output face_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "face_rects_from_detections"
+  input_stream: "prev_face_rects_from_landmarks"
+  output_stream: "face_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.5
+    }
+  }
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Outputs each element of face_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_face_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:face_rects"
+  input_stream: "CLONE:0:image"
+  input_stream: "CLONE:1:image_size"
+  output_stream: "ITEM:face_rect"
+  output_stream: "CLONE:0:landmarks_loop_image"
+  output_stream: "CLONE:1:landmarks_loop_image_size"
+  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
+}
+
+# Detects face landmarks within specified region of interest of the image.
+node {
+  calculator: "FaceLandmarkSideModelCpu"
+  input_stream: "IMAGE:landmarks_loop_image"
+  input_stream: "ROI:face_rect"
+  input_side_packet: "MODEL:face_landmark_model"
+  output_stream: "LANDMARKS:face_landmarks"
+}
+
+# Calculates region of interest based on face landmarks, so that can be reused
+# for subsequent image.
+node {
+  calculator: "FaceLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:face_landmarks"
+  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
+  output_stream: "ROI:face_rect_from_landmarks"
+}
+
+# Collects a set of landmarks for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:face_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:multi_face_landmarks"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_landmarks"
+}
+
+# Caches face rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# face rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:face_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_side_model_gpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_side_model_gpu.pbtxt
new file mode 100644
index 0000000..9832c2f
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_front_side_model_gpu.pbtxt
@@ -0,0 +1,224 @@
+# MediaPipe graph to detect/predict face landmarks. (GPU input, and inference is
+# executed on GPU.) This graph tries to skip face detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceLandmarkFrontSideModelGpu"
+#     input_stream: "IMAGE:image"
+#     input_side_packet: "NUM_FACES:num_faces"
+#     input_side_packet: "MODEL:0:face_detection_model"
+#     input_side_packet: "MODEL:1:face_landmark_model"
+#     output_stream: "LANDMARKS:multi_face_landmarks"
+#   }
+
+type: "FaceLandmarkFrontSideModelGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Max number of faces to detect/track. (int)
+input_side_packet: "NUM_FACES:num_faces"
+# TfLite model to detect faces.
+# (std::unique_ptr<tflite::FlatBufferModel,
+#      std::function<void(tflite::FlatBufferModel*)>>)
+# NOTE: mediapipe/modules/face_detection/face_detection_short_range.tflite
+# model only, can be passed here, otherwise - results are undefined.
+input_side_packet: "MODEL:0:face_detection_model"
+# TfLite model to detect face landmarks.
+# (std::unique_ptr<tflite::FlatBufferModel,
+#      std::function<void(tflite::FlatBufferModel*)>>)
+# NOTE: mediapipe/modules/face_landmark/face_landmark.tflite model
+# only, can be passed here, otherwise - results are undefined.
+input_side_packet: "MODEL:1:face_landmark_model"
+
+# Collection of detected/predicted faces, each represented as a list of 468 face
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of faces detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_face_landmarks"
+
+# Extra outputs (for debugging, for instance).
+# Detected faces. (std::vector<Detection>)
+output_stream: "DETECTIONS:face_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+# Regions of interest calculated based on face detections.
+# (std::vector<NormalizedRect>)
+output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided num_faces.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:prev_face_rects_from_landmarks"
+  input_side_packet: "num_faces"
+  output_stream: "prev_has_enough_faces"
+}
+
+# Drops the incoming image if FaceLandmarkGpu was able to identify face presence
+# in the previous image. Otherwise, passes the incoming image through to trigger
+# a new round of face detection in FaceDetectionShortRangeGpu.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_faces"
+  output_stream: "gated_image"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects faces.
+node {
+  calculator: "FaceDetectionShortRangeSideModelGpu"
+  input_stream: "IMAGE:gated_image"
+  input_side_packet: "MODEL:face_detection_model"
+  output_stream: "DETECTIONS:all_face_detections"
+}
+
+# Makes sure there are no more detections than the provided num_faces.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "all_face_detections"
+  output_stream: "face_detections"
+  input_side_packet: "num_faces"
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:gated_image"
+  output_stream: "SIZE:gated_image_size"
+}
+
+# Outputs each element of face_detections at a fake timestamp for the rest of
+# the graph to process. Clones the image size packet for each face_detection at
+# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
+# for downstream calculators to inform them that all elements in the vector have
+# been processed.
+node {
+  calculator: "BeginLoopDetectionCalculator"
+  input_stream: "ITERABLE:face_detections"
+  input_stream: "CLONE:gated_image_size"
+  output_stream: "ITEM:face_detection"
+  output_stream: "CLONE:detections_loop_image_size"
+  output_stream: "BATCH_END:detections_loop_end_timestamp"
+}
+
+# Calculates region of interest based on face detections, so that can be used
+# to detect landmarks.
+node {
+  calculator: "FaceDetectionFrontDetectionToRoi"
+  input_stream: "DETECTION:face_detection"
+  input_stream: "IMAGE_SIZE:detections_loop_image_size"
+  output_stream: "ROI:face_rect_from_detection"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_detection"
+  input_stream: "BATCH_END:detections_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_detections"
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on face detections from the current image. This
+# calculator ensures that the output face_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "face_rects_from_detections"
+  input_stream: "prev_face_rects_from_landmarks"
+  output_stream: "face_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.5
+    }
+  }
+}
+
+# Calculate size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Outputs each element of face_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_face_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:face_rects"
+  input_stream: "CLONE:0:image"
+  input_stream: "CLONE:1:image_size"
+  output_stream: "ITEM:face_rect"
+  output_stream: "CLONE:0:landmarks_loop_image"
+  output_stream: "CLONE:1:landmarks_loop_image_size"
+  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
+}
+
+# Detects face landmarks within specified region of interest of the image.
+node {
+  calculator: "FaceLandmarkSideModelGpu"
+  input_stream: "IMAGE:landmarks_loop_image"
+  input_stream: "ROI:face_rect"
+  input_side_packet: "MODEL:face_landmark_model"
+  output_stream: "LANDMARKS:face_landmarks"
+}
+
+# Calculates region of interest based on face landmarks, so that can be reused
+# for subsequent image.
+node {
+  calculator: "FaceLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:face_landmarks"
+  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
+  output_stream: "ROI:face_rect_from_landmarks"
+}
+
+# Collects a set of landmarks for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:face_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:multi_face_landmarks"
+}
+
+# Collects a NormalizedRect for each face into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:face_rect_from_landmarks"
+  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
+  output_stream: "ITERABLE:face_rects_from_landmarks"
+}
+
+# Caches face rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# face rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:face_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt
new file mode 100644
index 0000000..854ceaf
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt
@@ -0,0 +1,185 @@
+# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
+# executed on CPU.)
+#
+# It is required that "face_landmark.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark.tflite"
+# path during execution if `with_attention` is not set or set to `false`.
+#
+# It is required that "face_landmark_with_attention.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
+# path during execution if `with_attention` is set to `true`.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "FaceLandmarkGpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:face_roi"
+#     input_side_packet: "WITH_ATTENTION:with_attention"
+#     output_stream: "LANDMARKS:face_landmarks"
+#   }
+
+type: "FaceLandmarkGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a face is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+# Whether to run face mesh model with attention on lips and eyes. (bool)
+# Attention provides more accuracy on lips and eye regions as well as iris
+# landmarks.
+input_side_packet: "WITH_ATTENTION:with_attention"
+
+# 468 or 478 facial landmarks within the given ROI. (NormalizedLandmarkList)
+#
+# Number of landmarks depends on the WITH_ATTENTION flag. If it's `true` - then
+# there will be 478 landmarks with refined lips, eyes and irises (10 extra
+# landmarks are for irises), otherwise 468 non-refined landmarks are returned.
+#
+# NOTE: if a face is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:face_landmarks"
+
+# Transforms the input image into a 192x192 tensor.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:input_tensors"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 192
+      output_tensor_height: 192
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Loads the face landmarks TF Lite model.
+node {
+  calculator: "FaceLandmarksModelLoader"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_side_packet: "MODEL:model"
+}
+
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "op_resolver"
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of GPU tensors representing, for instance, detection boxes/keypoints
+# and scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  input_side_packet: "MODEL:model"
+  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      # Do not remove. Used for generation of XNNPACK/NNAPI graphs.
+    }
+  }
+}
+
+# Splits a vector of tensors into landmark tensors and face flag tensor.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:with_attention"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "face_flag_tensor"
+  options {
+    [mediapipe.SwitchContainerOptions.ext] {
+      contained_node: {
+        calculator: "SplitTensorVectorCalculator"
+        options: {
+          [mediapipe.SplitVectorCalculatorOptions.ext] {
+            ranges: { begin: 0 end: 1 }
+            ranges: { begin: 1 end: 2 }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "SplitTensorVectorCalculator"
+        options: {
+          [mediapipe.SplitVectorCalculatorOptions.ext] {
+            ranges: { begin: 0 end: 6 }
+            ranges: { begin: 6 end: 7 }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Converts the face-flag tensor into a float that represents the confidence
+# score of face presence.
+node {
+  calculator: "TensorsToFloatsCalculator"
+  input_stream: "TENSORS:face_flag_tensor"
+  output_stream: "FLOAT:face_presence_score"
+  options: {
+    [mediapipe.TensorsToFloatsCalculatorOptions.ext] {
+      activation: SIGMOID
+    }
+  }
+}
+
+# Applies a threshold to the confidence score to determine whether a face is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:face_presence_score"
+  output_stream: "FLAG:face_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.5
+    }
+  }
+}
+
+# Drop landmarks tensors if face is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:face_presence"
+  output_stream: "ensured_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:with_attention"
+  input_stream: "TENSORS:ensured_landmark_tensors"
+  output_stream: "LANDMARKS:landmarks"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      contained_node: {
+        calculator: "TensorsToFaceLandmarks"
+      }
+      contained_node: {
+        calculator: "TensorsToFaceLandmarksWithAttention"
+      }
+    }
+  }
+}
+
+# Projects the landmarks from the cropped face image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:face_landmarks"
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_landmarks_to_roi.pbtxt b/mediapipe/modules/face_landmark/face_landmark_landmarks_to_roi.pbtxt
new file mode 100644
index 0000000..9f634b0
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmark_landmarks_to_roi.pbtxt
@@ -0,0 +1,54 @@
+# MediaPipe graph to calculate face region of interest (ROI) from landmarks
+# detected by "FaceLandmarkCpu" or "FaceLandmarkGpu".
+#
+# NOTE: this graph is subject to change and should not be used directly.
+
+type: "FaceLandmarkLandmarksToRoi"
+
+# Normalized landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:landmarks"
+# Frame size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+# ROI according to landmarks. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts face landmarks to a detection that tightly encloses all landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  output_stream: "DETECTION:face_detection"
+}
+
+# Converts the face detection into a rectangle (normalized by image size)
+# that encloses the face and is rotated such that the line connecting left side
+# of the left eye and right side of the right eye is aligned with the X-axis of
+# the rectangle.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTION:face_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:face_rect_from_landmarks"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 33  # Left side of left eye.
+      rotation_vector_end_keypoint_index: 263  # Right side of right eye.
+      rotation_vector_target_angle_degrees: 0
+    }
+  }
+}
+
+# Expands the face rectangle so that in the next video image it's likely to
+# still contain the face even with some motion.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:face_rect_from_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.5
+      scale_y: 1.5
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/face_landmark/face_landmark_with_attention.tflite b/mediapipe/modules/face_landmark/face_landmark_with_attention.tflite
new file mode 100755
index 0000000..fe0a93a
Binary files /dev/null and b/mediapipe/modules/face_landmark/face_landmark_with_attention.tflite differ
diff --git a/mediapipe/modules/face_landmark/face_landmarks_model_loader.pbtxt b/mediapipe/modules/face_landmark/face_landmarks_model_loader.pbtxt
new file mode 100644
index 0000000..ecac1a6
--- /dev/null
+++ b/mediapipe/modules/face_landmark/face_landmarks_model_loader.pbtxt
@@ -0,0 +1,58 @@
+# MediaPipe graph to load a selected face landmarks TF Lite model.
+
+type: "FaceLandmarksModelLoader"
+
+# Whether to run face mesh model with attention on lips and eyes. (bool)
+# Attention provides more accuracy on lips and eye regions as well as iris
+# landmarks.
+input_side_packet: "WITH_ATTENTION:with_attention"
+
+# TF Lite model represented as a FlatBuffer.
+# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
+output_side_packet: "MODEL:model"
+
+# Determines path to the desired face landmark model file based on specification
+# in the input side packet.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:with_attention"
+  output_side_packet: "PACKET:model_path"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/face_landmark/face_landmark.tflite"
+            }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Loads the file in the specified path into a blob.
+node {
+  calculator: "LocalFileContentsCalculator"
+  input_side_packet: "FILE_PATH:model_path"
+  output_side_packet: "CONTENTS:model_blob"
+}
+
+# Converts the input blob into a TF Lite model.
+node {
+  calculator: "TfLiteModelCalculator"
+  input_side_packet: "MODEL_BLOB:model_blob"
+  output_side_packet: "MODEL:model"
+}
diff --git a/mediapipe/modules/face_landmark/tensors_to_face_landmarks.pbtxt b/mediapipe/modules/face_landmark/tensors_to_face_landmarks.pbtxt
new file mode 100644
index 0000000..0adbdf3
--- /dev/null
+++ b/mediapipe/modules/face_landmark/tensors_to_face_landmarks.pbtxt
@@ -0,0 +1,24 @@
+# MediaPipe graph to transform single tensor into 468 facial landmarks.
+
+type: "TensorsToFaceLandmarks"
+
+# Vector with a single tensor that contains 468 landmarks. (std::vector<Tensor>)
+input_stream: "TENSORS:tensors"
+
+# 468 facial landmarks (NormalizedLandmarkList)
+output_stream: "LANDMARKS:landmarks"
+
+# Decodes the landmark tensors into a vector of lanmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:tensors"
+  output_stream: "NORM_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 468
+      input_image_width: 192
+      input_image_height: 192
+    }
+  }
+}
diff --git a/mediapipe/modules/face_landmark/tensors_to_face_landmarks_with_attention.pbtxt b/mediapipe/modules/face_landmark/tensors_to_face_landmarks_with_attention.pbtxt
new file mode 100644
index 0000000..4f9b994
--- /dev/null
+++ b/mediapipe/modules/face_landmark/tensors_to_face_landmarks_with_attention.pbtxt
@@ -0,0 +1,299 @@
+# MediaPipe graph to transform model output tensors into 478 facial landmarks
+# with refined lips, eyes and irises.
+
+type: "TensorsToFaceLandmarksWithAttention"
+
+# Vector with a six tensors to parse landmarks from. (std::vector<Tensor>)
+# Landmark tensors order:
+#   - mesh_tensor
+#   - lips_tensor
+#   - left_eye_tensor
+#   - right_eye_tensor
+#   - left_iris_tensor
+#   - right_iris_tensor
+input_stream: "TENSORS:tensors"
+
+# 478 facial landmarks (NormalizedLandmarkList)
+output_stream: "LANDMARKS:landmarks"
+
+# Splits a vector of tensors into multiple vectors.
+node {
+  calculator: "SplitTensorVectorCalculator"
+  input_stream: "tensors"
+  output_stream: "mesh_tensor"
+  output_stream: "lips_tensor"
+  output_stream: "left_eye_tensor"
+  output_stream: "right_eye_tensor"
+  output_stream: "left_iris_tensor"
+  output_stream: "right_iris_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+      ranges: { begin: 2 end: 3 }
+      ranges: { begin: 3 end: 4 }
+      ranges: { begin: 4 end: 5 }
+      ranges: { begin: 5 end: 6 }
+    }
+  }
+}
+
+# Decodes mesh landmarks tensor into a vector of normalized lanmarks.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:mesh_tensor"
+  output_stream: "NORM_LANDMARKS:mesh_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 468
+      input_image_width: 192
+      input_image_height: 192
+    }
+  }
+}
+
+# Decodes lips landmarks tensor into a vector of normalized lanmarks.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:lips_tensor"
+  output_stream: "NORM_LANDMARKS:lips_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 80
+      input_image_width: 192
+      input_image_height: 192
+    }
+  }
+}
+
+# Decodes left eye landmarks tensor into a vector of normalized lanmarks.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:left_eye_tensor"
+  output_stream: "NORM_LANDMARKS:left_eye_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 71
+      input_image_width: 192
+      input_image_height: 192
+    }
+  }
+}
+
+# Decodes right eye landmarks tensor into a vector of normalized lanmarks.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:right_eye_tensor"
+  output_stream: "NORM_LANDMARKS:right_eye_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 71
+      input_image_width: 192
+      input_image_height: 192
+    }
+  }
+}
+
+# Decodes left iris landmarks tensor into a vector of normalized lanmarks.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:left_iris_tensor"
+  output_stream: "NORM_LANDMARKS:left_iris_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 5
+      input_image_width: 192
+      input_image_height: 192
+    }
+  }
+}
+
+# Decodes right iris landmarks tensor into a vector of normalized lanmarks.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:right_iris_tensor"
+  output_stream: "NORM_LANDMARKS:right_iris_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 5
+      input_image_width: 192
+      input_image_height: 192
+    }
+  }
+}
+
+# Refine mesh landmarks with lips, eyes and irises.
+node {
+  calculator: "LandmarksRefinementCalculator"
+  input_stream: "LANDMARKS:0:mesh_landmarks"
+  input_stream: "LANDMARKS:1:lips_landmarks"
+  input_stream: "LANDMARKS:2:left_eye_landmarks"
+  input_stream: "LANDMARKS:3:right_eye_landmarks"
+  input_stream: "LANDMARKS:4:left_iris_landmarks"
+  input_stream: "LANDMARKS:5:right_iris_landmarks"
+  output_stream: "REFINED_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.LandmarksRefinementCalculatorOptions.ext] {
+      # 0 - mesh
+      refinement: {
+        indexes_mapping: [
+          0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+          20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
+          37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
+          54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
+          71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
+          88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,
+          104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,
+          118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
+          132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,
+          146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
+          160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,
+          174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187,
+          188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201,
+          202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215,
+          216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
+          230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243,
+          244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,
+          258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,
+          272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
+          286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
+          300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,
+          314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327,
+          328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341,
+          342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355,
+          356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369,
+          370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383,
+          384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397,
+          398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,
+          412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425,
+          426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439,
+          440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453,
+          454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467
+        ]
+        z_refinement: { copy {} }
+      }
+      # 1 - lips
+      refinement: {
+        indexes_mapping: [
+          # Lower outer.
+          61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291,
+          # Upper outer (excluding corners).
+          185, 40, 39, 37, 0, 267, 269, 270, 409,
+          # Lower inner.
+          78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308,
+          # Upper inner (excluding corners).
+          191, 80, 81, 82, 13, 312, 311, 310, 415,
+          # Lower semi-outer.
+          76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306,
+          # Upper semi-outer (excluding corners).
+          184, 74, 73, 72, 11, 302, 303, 304, 408,
+          # Lower semi-inner.
+          62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292,
+          # Upper semi-inner (excluding corners).
+          183, 42, 41, 38, 12, 268, 271, 272, 407
+        ]
+        z_refinement: { none {} }
+      }
+      # 2 - left eye
+      refinement: {
+        indexes_mapping: [
+          # Lower contour.
+          33, 7, 163, 144, 145, 153, 154, 155, 133,
+          # upper contour (excluding corners).
+          246, 161, 160, 159, 158, 157, 173,
+          # Halo x2 lower contour.
+          130, 25, 110, 24, 23, 22, 26, 112, 243,
+          # Halo x2 upper contour (excluding corners).
+          247, 30, 29, 27, 28, 56, 190,
+          # Halo x3 lower contour.
+          226, 31, 228, 229, 230, 231, 232, 233, 244,
+          # Halo x3 upper contour (excluding corners).
+          113, 225, 224, 223, 222, 221, 189,
+          # Halo x4 upper contour (no lower because of mesh structure) or
+          # eyebrow inner contour.
+          35, 124, 46, 53, 52, 65,
+          # Halo x5 lower contour.
+          143, 111, 117, 118, 119, 120, 121, 128, 245,
+          # Halo x5 upper contour (excluding corners) or eyebrow outer contour.
+          156, 70, 63, 105, 66, 107, 55, 193
+        ]
+        z_refinement: { none {} }
+      }
+      # 3 - right eye
+      refinement: {
+        indexes_mapping: [
+          # Lower contour.
+          263, 249, 390, 373, 374, 380, 381, 382, 362,
+          # Upper contour (excluding corners).
+          466, 388, 387, 386, 385, 384, 398,
+          # Halo x2 lower contour.
+          359, 255, 339, 254, 253, 252, 256, 341, 463,
+          # Halo x2 upper contour (excluding corners).
+          467, 260, 259, 257, 258, 286, 414,
+          # Halo x3 lower contour.
+          446, 261, 448, 449, 450, 451, 452, 453, 464,
+          # Halo x3 upper contour (excluding corners).
+          342, 445, 444, 443, 442, 441, 413,
+          # Halo x4 upper contour (no lower because of mesh structure) or
+          # eyebrow inner contour.
+          265, 353, 276, 283, 282, 295,
+          # Halo x5 lower contour.
+          372, 340, 346, 347, 348, 349, 350, 357, 465,
+          # Halo x5 upper contour (excluding corners) or eyebrow outer contour.
+          383, 300, 293, 334, 296, 336, 285, 417
+        ]
+        z_refinement: { none {} }
+      }
+      # 4 - left iris
+      refinement: {
+        indexes_mapping: [
+          # Center.
+          468,
+          # Iris right edge.
+          469,
+          # Iris top edge.
+          470,
+          # Iris left edge.
+          471,
+          # Iris bottom edge.
+          472
+        ]
+        z_refinement: {
+          assign_average: {
+            indexes_for_average: [
+              # Lower contour.
+              33, 7, 163, 144, 145, 153, 154, 155, 133,
+              # Upper contour (excluding corners).
+              246, 161, 160, 159, 158, 157, 173
+            ]
+          }
+        }
+      }
+      # 5 - right iris
+      refinement: {
+        indexes_mapping: [
+          # Center.
+          473,
+          # Iris right edge.
+          474,
+          # Iris top edge.
+          475,
+          # Iris left edge.
+          476,
+          # Iris bottom edge.
+          477
+        ]
+        z_refinement: {
+          assign_average: {
+            indexes_for_average: [
+              # Lower contour.
+              263, 249, 390, 373, 374, 380, 381, 382, 362,
+              # Upper contour (excluding corners).
+              466, 388, 387, 386, 385, 384, 398
+            ]
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/mediapipe/modules/hand_landmark/BUILD b/mediapipe/modules/hand_landmark/BUILD
new file mode 100644
index 0000000..b28dc78
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/BUILD
@@ -0,0 +1,171 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+exports_files([
+    "hand_landmark_full.tflite",
+    "hand_landmark_lite.tflite",
+    "handedness.txt",
+])
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_model_loader",
+    graph = "hand_landmark_model_loader.pbtxt",
+    register_as = "HandLandmarkModelLoader",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/tflite:tflite_model_calculator",
+        "//mediapipe/calculators/util:local_file_contents_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_cpu",
+    graph = "hand_landmark_cpu.pbtxt",
+    register_as = "HandLandmarkCpu",
+    deps = [
+        ":hand_landmark_model_loader",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_classification_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+        "//mediapipe/calculators/util:world_landmark_projection_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_gpu",
+    graph = "hand_landmark_gpu.pbtxt",
+    register_as = "HandLandmarkGpu",
+    deps = [
+        ":hand_landmark_model_loader",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_classification_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+        "//mediapipe/calculators/util:world_landmark_projection_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_tracking_gpu",
+    graph = "hand_landmark_tracking_gpu.pbtxt",
+    register_as = "HandLandmarkTrackingGpu",
+    deps = [
+        ":hand_landmark_gpu",
+        ":hand_landmark_landmarks_to_roi",
+        ":palm_detection_detection_to_roi",
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:association_norm_rect_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/calculators/util:filter_collection_calculator",
+        "//mediapipe/modules/palm_detection:palm_detection_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_tracking_cpu_image",
+    graph = "hand_landmark_tracking_cpu_image.pbtxt",
+    register_as = "HandLandmarkTrackingCpuImage",
+    deps = [
+        ":hand_landmark_tracking_cpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_tracking_gpu_image",
+    graph = "hand_landmark_tracking_gpu_image.pbtxt",
+    register_as = "HandLandmarkTrackingGpuImage",
+    deps = [
+        ":hand_landmark_tracking_gpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_tracking_cpu",
+    graph = "hand_landmark_tracking_cpu.pbtxt",
+    register_as = "HandLandmarkTrackingCpu",
+    deps = [
+        ":hand_landmark_cpu",
+        ":hand_landmark_landmarks_to_roi",
+        ":palm_detection_detection_to_roi",
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:association_norm_rect_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/calculators/util:filter_collection_calculator",
+        "//mediapipe/modules/palm_detection:palm_detection_cpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "palm_detection_detection_to_roi",
+    graph = "palm_detection_detection_to_roi.pbtxt",
+    register_as = "PalmDetectionDetectionToRoi",
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmark_landmarks_to_roi",
+    graph = "hand_landmark_landmarks_to_roi.pbtxt",
+    register_as = "HandLandmarkLandmarksToRoi",
+    deps = [
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+        "//mediapipe/modules/hand_landmark/calculators:hand_landmarks_to_rect_calculator",
+    ],
+)
diff --git a/mediapipe/modules/hand_landmark/README.md b/mediapipe/modules/hand_landmark/README.md
new file mode 100644
index 0000000..31fe6f7
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/README.md
@@ -0,0 +1,8 @@
+# hand_landmark
+
+Subgraphs|Details
+:--- | :---
+[`HandLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt)| Detects landmarks of a single hand. (CPU input.)
+[`HandLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt)| Detects landmarks of a single hand. (GPU input.)
+[`HandLandmarkTrackingCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt)| Detects and tracks landmarks of multiple hands. (CPU input.)
+[`HandLandmarkTrackingGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt)| Detects and tracks landmarks of multiple hands. (GPU input.)
diff --git a/mediapipe/modules/hand_landmark/calculators/BUILD b/mediapipe/modules/hand_landmark/calculators/BUILD
new file mode 100644
index 0000000..b2a8efe
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/calculators/BUILD
@@ -0,0 +1,33 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "hand_landmarks_to_rect_calculator",
+    srcs = ["hand_landmarks_to_rect_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_options_cc_proto",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/formats:location_data_cc_proto",
+        "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ],
+    alwayslink = 1,
+)
diff --git a/mediapipe/modules/hand_landmark/calculators/hand_landmarks_to_rect_calculator.cc b/mediapipe/modules/hand_landmark/calculators/hand_landmarks_to_rect_calculator.cc
new file mode 100644
index 0000000..3e3f5c8
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/calculators/hand_landmarks_to_rect_calculator.cc
@@ -0,0 +1,167 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include <cmath>
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_options.pb.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/formats/rect.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kNormalizedLandmarksTag[] = "NORM_LANDMARKS";
+constexpr char kNormRectTag[] = "NORM_RECT";
+constexpr char kImageSizeTag[] = "IMAGE_SIZE";
+constexpr int kWristJoint = 0;
+constexpr int kMiddleFingerPIPJoint = 6;
+constexpr int kIndexFingerPIPJoint = 4;
+constexpr int kRingFingerPIPJoint = 8;
+constexpr float kTargetAngle = M_PI * 0.5f;
+
+inline float NormalizeRadians(float angle) {
+  return angle - 2 * M_PI * std::floor((angle - (-M_PI)) / (2 * M_PI));
+}
+
+float ComputeRotation(const NormalizedLandmarkList& landmarks,
+                      const std::pair<int, int>& image_size) {
+  const float x0 = landmarks.landmark(kWristJoint).x() * image_size.first;
+  const float y0 = landmarks.landmark(kWristJoint).y() * image_size.second;
+
+  float x1 = (landmarks.landmark(kIndexFingerPIPJoint).x() +
+              landmarks.landmark(kRingFingerPIPJoint).x()) /
+             2.f;
+  float y1 = (landmarks.landmark(kIndexFingerPIPJoint).y() +
+              landmarks.landmark(kRingFingerPIPJoint).y()) /
+             2.f;
+  x1 = (x1 + landmarks.landmark(kMiddleFingerPIPJoint).x()) / 2.f *
+       image_size.first;
+  y1 = (y1 + landmarks.landmark(kMiddleFingerPIPJoint).y()) / 2.f *
+       image_size.second;
+
+  const float rotation =
+      NormalizeRadians(kTargetAngle - std::atan2(-(y1 - y0), x1 - x0));
+  return rotation;
+}
+
+absl::Status NormalizedLandmarkListToRect(
+    const NormalizedLandmarkList& landmarks,
+    const std::pair<int, int>& image_size, NormalizedRect* rect) {
+  const float rotation = ComputeRotation(landmarks, image_size);
+  const float reverse_angle = NormalizeRadians(-rotation);
+
+  // Find boundaries of landmarks.
+  float max_x = std::numeric_limits<float>::min();
+  float max_y = std::numeric_limits<float>::min();
+  float min_x = std::numeric_limits<float>::max();
+  float min_y = std::numeric_limits<float>::max();
+  for (int i = 0; i < landmarks.landmark_size(); ++i) {
+    max_x = std::max(max_x, landmarks.landmark(i).x());
+    max_y = std::max(max_y, landmarks.landmark(i).y());
+    min_x = std::min(min_x, landmarks.landmark(i).x());
+    min_y = std::min(min_y, landmarks.landmark(i).y());
+  }
+  const float axis_aligned_center_x = (max_x + min_x) / 2.f;
+  const float axis_aligned_center_y = (max_y + min_y) / 2.f;
+
+  // Find boundaries of rotated landmarks.
+  max_x = std::numeric_limits<float>::min();
+  max_y = std::numeric_limits<float>::min();
+  min_x = std::numeric_limits<float>::max();
+  min_y = std::numeric_limits<float>::max();
+  for (int i = 0; i < landmarks.landmark_size(); ++i) {
+    const float original_x =
+        (landmarks.landmark(i).x() - axis_aligned_center_x) * image_size.first;
+    const float original_y =
+        (landmarks.landmark(i).y() - axis_aligned_center_y) * image_size.second;
+
+    const float projected_x = original_x * std::cos(reverse_angle) -
+                              original_y * std::sin(reverse_angle);
+    const float projected_y = original_x * std::sin(reverse_angle) +
+                              original_y * std::cos(reverse_angle);
+
+    max_x = std::max(max_x, projected_x);
+    max_y = std::max(max_y, projected_y);
+    min_x = std::min(min_x, projected_x);
+    min_y = std::min(min_y, projected_y);
+  }
+  const float projected_center_x = (max_x + min_x) / 2.f;
+  const float projected_center_y = (max_y + min_y) / 2.f;
+
+  const float center_x = projected_center_x * std::cos(rotation) -
+                         projected_center_y * std::sin(rotation) +
+                         image_size.first * axis_aligned_center_x;
+  const float center_y = projected_center_x * std::sin(rotation) +
+                         projected_center_y * std::cos(rotation) +
+                         image_size.second * axis_aligned_center_y;
+  const float width = (max_x - min_x) / image_size.first;
+  const float height = (max_y - min_y) / image_size.second;
+
+  rect->set_x_center(center_x / image_size.first);
+  rect->set_y_center(center_y / image_size.second);
+  rect->set_width(width);
+  rect->set_height(height);
+  rect->set_rotation(rotation);
+
+  return absl::OkStatus();
+}
+
+}  // namespace
+
+// A calculator that converts subset of hand landmarks to a bounding box
+// NormalizedRect. The rotation angle of the bounding box is computed based on
+// 1) the wrist joint and 2) the average of PIP joints of index finger, middle
+// finger and ring finger. After rotation, the vector from the wrist to the mean
+// of PIP joints is expected to be vertical with wrist at the bottom and the
+// mean of PIP joints at the top.
+class HandLandmarksToRectCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->Inputs().Tag(kNormalizedLandmarksTag).Set<NormalizedLandmarkList>();
+    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
+    cc->Outputs().Tag(kNormRectTag).Set<NormalizedRect>();
+    return absl::OkStatus();
+  }
+
+  absl::Status Open(CalculatorContext* cc) override {
+    cc->SetOffset(TimestampDiff(0));
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    if (cc->Inputs().Tag(kNormalizedLandmarksTag).IsEmpty()) {
+      return absl::OkStatus();
+    }
+    RET_CHECK(!cc->Inputs().Tag(kImageSizeTag).IsEmpty());
+
+    std::pair<int, int> image_size =
+        cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
+    const auto& landmarks =
+        cc->Inputs().Tag(kNormalizedLandmarksTag).Get<NormalizedLandmarkList>();
+    auto output_rect = absl::make_unique<NormalizedRect>();
+    MP_RETURN_IF_ERROR(
+        NormalizedLandmarkListToRect(landmarks, image_size, output_rect.get()));
+    cc->Outputs()
+        .Tag(kNormRectTag)
+        .Add(output_rect.release(), cc->InputTimestamp());
+
+    return absl::OkStatus();
+  }
+};
+REGISTER_CALCULATOR(HandLandmarksToRectCalculator);
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt
new file mode 100644
index 0000000..6ecbfad
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt
@@ -0,0 +1,219 @@
+# MediaPipe graph to detect/predict hand landmarks on CPU.
+
+type: "HandLandmarkCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a palm/hand is located.
+# (NormalizedRect)
+input_stream: "ROI:hand_rect"
+
+# Complexity of the hand landmark model: 0 or 1. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# 21 hand landmarks within the given ROI. (NormalizedLandmarkList)
+# NOTE: if a hand is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:hand_landmarks"
+
+# Hand world landmarks within the given ROI. (LandmarkList)
+# World landmarks are real-world 3D coordinates in meters with the origin in the
+# center of the given ROI.
+#
+# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
+# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
+# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
+# the 3D object itself.
+output_stream: "WORLD_LANDMARKS:hand_world_landmarks"
+
+# Handedness of the detected hand (i.e. is hand left or right).
+# (ClassificationList)
+output_stream: "HANDEDNESS:handedness"
+
+# Transforms a region of image into a 224x224 tensor while keeping the aspect
+# ratio, and therefore may result in potential letterboxing.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:image"
+  input_stream: "NORM_RECT:hand_rect"
+  output_stream: "TENSORS:input_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 224
+      output_tensor_height: 224
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+    }
+  }
+}
+
+# Loads the hand landmark TF Lite model.
+node {
+  calculator: "HandLandmarkModelLoader"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_side_packet: "MODEL:model"
+  input_stream: "TENSORS:input_tensor"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      delegate {
+        xnnpack {}
+      }
+    }
+  }
+}
+
+# Splits a vector of tensors to multiple vectors according to the ranges
+# specified in option.
+node {
+  calculator: "SplitTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "hand_flag_tensor"
+  output_stream: "handedness_tensor"
+  output_stream: "world_landmark_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+      ranges: { begin: 2 end: 3 }
+      ranges: { begin: 3 end: 4 }
+    }
+  }
+}
+
+# Converts the hand-flag tensor into a float that represents the confidence
+# score of hand presence.
+node {
+  calculator: "TensorsToFloatsCalculator"
+  input_stream: "TENSORS:hand_flag_tensor"
+  output_stream: "FLOAT:hand_presence_score"
+}
+
+# Applies a threshold to the confidence score to determine whether a hand is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:hand_presence_score"
+  output_stream: "FLAG:hand_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.5
+    }
+  }
+}
+
+# Drops handedness tensor if hand is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "handedness_tensor"
+  input_stream: "ALLOW:hand_presence"
+  output_stream: "ensured_handedness_tensor"
+}
+
+# Converts the handedness tensor into a float that represents the classification
+# score of handedness.
+node {
+  calculator: "TensorsToClassificationCalculator"
+  input_stream: "TENSORS:ensured_handedness_tensor"
+  output_stream: "CLASSIFICATIONS:handedness"
+  options: {
+    [mediapipe.TensorsToClassificationCalculatorOptions.ext] {
+      top_k: 1
+      label_map_path: "mediapipe/modules/hand_landmark/handedness.txt"
+      binary_classification: true
+    }
+  }
+}
+
+# Drops landmarks tensors if hand is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:hand_presence"
+  output_stream: "ensured_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a list of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_landmark_tensors"
+  output_stream: "NORM_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 21
+      input_image_width: 224
+      input_image_height: 224
+      # The additional scaling factor is used to account for the Z coordinate
+      # distribution in the training data.
+      normalize_z: 0.4
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (hand
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:scaled_landmarks"
+}
+
+# Projects the landmarks from the cropped hand image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:scaled_landmarks"
+  input_stream: "NORM_RECT:hand_rect"
+  output_stream: "NORM_LANDMARKS:hand_landmarks"
+}
+
+# Drops world landmarks tensors if hand is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "world_landmark_tensor"
+  input_stream: "ALLOW:hand_presence"
+  output_stream: "ensured_world_landmark_tensor"
+}
+
+# Decodes the landmark tensors into a list of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_world_landmark_tensor"
+  output_stream: "LANDMARKS:unprojected_world_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 21
+    }
+  }
+}
+
+# Projects the world landmarks from the cropped hand image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "WorldLandmarkProjectionCalculator"
+  input_stream: "LANDMARKS:unprojected_world_landmarks"
+  input_stream: "NORM_RECT:hand_rect"
+  output_stream: "LANDMARKS:hand_world_landmarks"
+}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_full.tflite b/mediapipe/modules/hand_landmark/hand_landmark_full.tflite
new file mode 100755
index 0000000..a2b0114
Binary files /dev/null and b/mediapipe/modules/hand_landmark/hand_landmark_full.tflite differ
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt
new file mode 100644
index 0000000..033ad44
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt
@@ -0,0 +1,213 @@
+# MediaPipe graph to detect/predict hand landmarks on CPU.
+
+type: "HandLandmarkGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a palm/hand is located.
+# (NormalizedRect)
+input_stream: "ROI:hand_rect"
+
+# Complexity of the hand landmark model: 0 or 1. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# 21 hand landmarks within the given ROI. (NormalizedLandmarkList)
+# NOTE: if a hand is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:hand_landmarks"
+
+# Hand world landmarks within the given ROI. (LandmarkList)
+# World landmarks are real-world 3D coordinates in meters with the origin in the
+# center of the given ROI.
+#
+# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
+# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
+# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
+# the 3D object itself.
+output_stream: "WORLD_LANDMARKS:hand_world_landmarks"
+
+# Handedness of the detected hand (i.e. is hand left or right).
+# (ClassificationList)
+output_stream: "HANDEDNESS:handedness"
+
+# Transforms a region of image into a 224x224 tensor while keeping the aspect
+# ratio, and therefore may result in potential letterboxing.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:image"
+  input_stream: "NORM_RECT:hand_rect"
+  output_stream: "TENSORS:input_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 224
+      output_tensor_height: 224
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Loads the hand landmark TF Lite model.
+node {
+  calculator: "HandLandmarkModelLoader"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_side_packet: "MODEL:model"
+  input_stream: "TENSORS:input_tensor"
+  output_stream: "TENSORS:output_tensors"
+}
+
+# Splits a vector of tensors to multiple vectors according to the ranges
+# specified in option.
+node {
+  calculator: "SplitTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "hand_flag_tensor"
+  output_stream: "handedness_tensor"
+  output_stream: "world_landmark_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+      ranges: { begin: 2 end: 3 }
+      ranges: { begin: 3 end: 4 }
+    }
+  }
+}
+
+# Converts the hand-flag tensor into a float that represents the confidence
+# score of hand presence.
+node {
+  calculator: "TensorsToFloatsCalculator"
+  input_stream: "TENSORS:hand_flag_tensor"
+  output_stream: "FLOAT:hand_presence_score"
+}
+
+# Applies a threshold to the confidence score to determine whether a hand is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:hand_presence_score"
+  output_stream: "FLAG:hand_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.5
+    }
+  }
+}
+
+# Drops handedness tensor if hand is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "handedness_tensor"
+  input_stream: "ALLOW:hand_presence"
+  output_stream: "ensured_handedness_tensor"
+}
+
+# Converts the handedness tensor into a float that represents the classification
+# score of handedness.
+node {
+  calculator: "TensorsToClassificationCalculator"
+  input_stream: "TENSORS:ensured_handedness_tensor"
+  output_stream: "CLASSIFICATIONS:handedness"
+  options: {
+    [mediapipe.TensorsToClassificationCalculatorOptions.ext] {
+      top_k: 1
+      label_map_path: "mediapipe/modules/hand_landmark/handedness.txt"
+      binary_classification: true
+    }
+  }
+}
+
+# Drops landmarks tensors if hand is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:hand_presence"
+  output_stream: "ensured_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a list of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_landmark_tensors"
+  output_stream: "NORM_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 21
+      input_image_width: 224
+      input_image_height: 224
+      # The additional scaling factor is used to account for the Z coordinate
+      # distribution in the training data.
+      normalize_z: 0.4
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (hand
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:scaled_landmarks"
+}
+
+# Projects the landmarks from the cropped hand image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:scaled_landmarks"
+  input_stream: "NORM_RECT:hand_rect"
+  output_stream: "NORM_LANDMARKS:hand_landmarks"
+}
+
+# Drops world landmarks tensors if hand is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "world_landmark_tensor"
+  input_stream: "ALLOW:hand_presence"
+  output_stream: "ensured_world_landmark_tensor"
+}
+
+# Decodes the landmark tensors into a list of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_world_landmark_tensor"
+  output_stream: "LANDMARKS:unprojected_world_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 21
+    }
+  }
+}
+
+# Projects the world landmarks from the cropped hand image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "WorldLandmarkProjectionCalculator"
+  input_stream: "LANDMARKS:unprojected_world_landmarks"
+  input_stream: "NORM_RECT:hand_rect"
+  output_stream: "LANDMARKS:hand_world_landmarks"
+}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_landmarks_to_roi.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_landmarks_to_roi.pbtxt
new file mode 100644
index 0000000..1d82d76
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_landmarks_to_roi.pbtxt
@@ -0,0 +1,63 @@
+# MediaPipe graph to calculate hand region of interest (ROI) from landmarks
+# detected by "HandLandmarkCpu" or "HandLandmarkGpu".
+
+type: "HandLandmarkLandmarksToRoi"
+
+# Normalized landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:landmarks"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI according to landmarks. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Extracts a subset of the hand landmarks that are relatively more stable across
+# frames (e.g. comparing to finger tips) for computing the bounding box. The box
+# will later be expanded to contain the entire hand. In this approach, it is
+# more robust to drastically changing hand size.
+# The landmarks extracted are: wrist, MCP/PIP of five fingers.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "landmarks"
+  output_stream: "partial_landmarks"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 4 }
+      ranges: { begin: 5 end: 7 }
+      ranges: { begin: 9 end: 11 }
+      ranges: { begin: 13 end: 15 }
+      ranges: { begin: 17 end: 19 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Converts the hand landmarks into a rectangle (normalized by image size)
+# that encloses the hand. The calculator uses a subset of all hand landmarks
+# extracted from SplitNormalizedLandmarkListCalculator above to
+# calculate the bounding box and the rotation of the output rectangle. Please
+# see the comments in the calculator for more detail.
+node {
+  calculator: "HandLandmarksToRectCalculator"
+  input_stream: "NORM_LANDMARKS:partial_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:hand_rect_from_landmarks"
+}
+
+# Expands the hand rectangle so that the box contains the entire hand and it's
+# big enough so that it's likely to still contain the hand even with some motion
+# in the next video frame .
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:hand_rect_from_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 2.0
+      scale_y: 2.0
+      shift_y: -0.1
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite b/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite
new file mode 100755
index 0000000..0a0a2ba
Binary files /dev/null and b/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite differ
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_model_loader.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_model_loader.pbtxt
new file mode 100644
index 0000000..c9ecf8a
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_model_loader.pbtxt
@@ -0,0 +1,63 @@
+# MediaPipe graph to load a selected hand landmark TF Lite model.
+
+type: "HandLandmarkModelLoader"
+
+# Complexity of the hand landmark model: 0 or 1. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# TF Lite model represented as a FlatBuffer.
+# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
+output_side_packet: "MODEL:model"
+
+# Determines path to the desired pose landmark model file.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "SELECT:model_complexity"
+  output_side_packet: "PACKET:model_path"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      select: 1
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/hand_landmark/hand_landmark_lite.tflite"
+            }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/hand_landmark/hand_landmark_full.tflite"
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Loads the file in the specified path into a blob.
+node {
+  calculator: "LocalFileContentsCalculator"
+  input_side_packet: "FILE_PATH:model_path"
+  output_side_packet: "CONTENTS:model_blob"
+  options: {
+    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
+      text_mode: false
+    }
+  }
+}
+
+# Converts the input blob into a TF Lite model.
+node {
+  calculator: "TfLiteModelCalculator"
+  input_side_packet: "MODEL_BLOB:model_blob"
+  output_side_packet: "MODEL:model"
+}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt
new file mode 100644
index 0000000..2ee8316
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt
@@ -0,0 +1,271 @@
+# MediaPipe graph to detect/predict hand landmarks on CPU.
+#
+# The procedure is done in two steps:
+# - locate palms/hands
+# - detect landmarks for each palm/hand.
+# This graph tries to skip palm detection as much as possible by reusing
+# previously detected/predicted landmarks for new images.
+
+type: "HandLandmarkTrackingCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Max number of hands to detect/track. (int)
+input_side_packet: "NUM_HANDS:num_hands"
+
+# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
+# well as inference latency generally go up with the model complexity. If
+# unspecified, functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of hands detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_hand_landmarks"
+
+# Collection of detected/predicted hand world landmarks.
+# (std::vector<LandmarkList>)
+#
+# World landmarks are real-world 3D coordinates in meters with the origin in the
+# center of the hand bounding box calculated from the landmarks.
+#
+# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
+# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
+# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
+# the 3D object itself.
+output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
+
+# Collection of handedness of the detected hands (i.e. is hand left or right),
+# each represented as a ClassificationList proto with a single Classification
+# entry. (std::vector<ClassificationList>)
+# Note that handedness is determined assuming the input image is mirrored,
+# i.e., taken with a front-facing/selfie camera with images flipped
+# horizontally.
+output_stream: "HANDEDNESS:multi_handedness"
+
+# Extra outputs (for debugging, for instance).
+# Detected palms. (std::vector<Detection>)
+output_stream: "PALM_DETECTIONS:palm_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_hand_rects_from_landmarks"
+  output_stream: "gated_prev_hand_rects_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided num_hands.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:gated_prev_hand_rects_from_landmarks"
+  input_side_packet: "num_hands"
+  output_stream: "prev_has_enough_hands"
+}
+
+# Drops the incoming image if enough hands have already been identified from the
+# previous image. Otherwise, passes the incoming image through to trigger a new
+# round of palm detection.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_hands"
+  output_stream: "palm_detection_image"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects palms.
+node {
+  calculator: "PalmDetectionCpu"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_stream: "IMAGE:palm_detection_image"
+  output_stream: "DETECTIONS:all_palm_detections"
+}
+
+# Makes sure there are no more detections than the provided num_hands.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "all_palm_detections"
+  output_stream: "palm_detections"
+  input_side_packet: "num_hands"
+}
+
+# Extracts image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:palm_detection_image"
+  output_stream: "SIZE:palm_detection_image_size"
+}
+
+# Outputs each element of palm_detections at a fake timestamp for the rest of
+# the graph to process. Clones the image size packet for each palm_detection at
+# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
+# for downstream calculators to inform them that all elements in the vector have
+# been processed.
+node {
+  calculator: "BeginLoopDetectionCalculator"
+  input_stream: "ITERABLE:palm_detections"
+  input_stream: "CLONE:palm_detection_image_size"
+  output_stream: "ITEM:palm_detection"
+  output_stream: "CLONE:image_size_for_palms"
+  output_stream: "BATCH_END:palm_detections_timestamp"
+}
+
+# Calculates region of interest (ROI) based on the specified palm.
+node {
+  calculator: "PalmDetectionDetectionToRoi"
+  input_stream: "DETECTION:palm_detection"
+  input_stream: "IMAGE_SIZE:image_size_for_palms"
+  output_stream: "ROI:hand_rect_from_palm_detection"
+}
+
+# Collects a NormalizedRect for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:hand_rect_from_palm_detection"
+  input_stream: "BATCH_END:palm_detections_timestamp"
+  output_stream: "ITERABLE:hand_rects_from_palm_detections"
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on palm detections from the current image. This
+# calculator ensures that the output hand_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "hand_rects_from_palm_detections"
+  input_stream: "gated_prev_hand_rects_from_landmarks"
+  output_stream: "hand_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.5
+    }
+  }
+}
+
+# Extracts image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Outputs each element of hand_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_hand_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:hand_rects"
+  input_stream: "CLONE:0:image"
+  input_stream: "CLONE:1:image_size"
+  output_stream: "ITEM:single_hand_rect"
+  output_stream: "CLONE:0:image_for_landmarks"
+  output_stream: "CLONE:1:image_size_for_landmarks"
+  output_stream: "BATCH_END:hand_rects_timestamp"
+}
+
+# Detect hand landmarks for the specific hand rect.
+node {
+  calculator: "HandLandmarkCpu"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_stream: "IMAGE:image_for_landmarks"
+  input_stream: "ROI:single_hand_rect"
+  output_stream: "LANDMARKS:single_hand_landmarks"
+  output_stream: "WORLD_LANDMARKS:single_hand_world_landmarks"
+  output_stream: "HANDEDNESS:single_handedness"
+}
+
+# Collects the handedness for each single hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs a vector of ClassificationList at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopClassificationListCalculator"
+  input_stream: "ITEM:single_handedness"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:multi_handedness"
+}
+
+# Calculate region of interest (ROI) based on detected hand landmarks to reuse
+# on the subsequent runs of the graph.
+node {
+  calculator: "HandLandmarkLandmarksToRoi"
+  input_stream: "IMAGE_SIZE:image_size_for_landmarks"
+  input_stream: "LANDMARKS:single_hand_landmarks"
+  output_stream: "ROI:single_hand_rect_from_landmarks"
+}
+
+# Collects a set of landmarks for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:single_hand_landmarks"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:multi_hand_landmarks"
+}
+
+# Collects a set of world landmarks for each hand into a vector. Upon receiving
+# the BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopLandmarkListVectorCalculator"
+  input_stream: "ITEM:single_hand_world_landmarks"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:multi_hand_world_landmarks"
+}
+
+# Collects a NormalizedRect for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:single_hand_rect_from_landmarks"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:hand_rects_from_landmarks"
+}
+
+# Caches hand rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# hand rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:hand_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_hand_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu_image.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu_image.pbtxt
new file mode 100644
index 0000000..0bdabb9
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu_image.pbtxt
@@ -0,0 +1,116 @@
+# MediaPipe graph to detect/predict hand landmarks on CPU.
+#
+# The procedure is done in two steps:
+# - locate palms/hands
+# - detect landmarks for each palm/hand.
+# This graph tries to skip palm detection as much as possible by reusing
+# previously detected/predicted landmarks for new images.
+
+type: "HandLandmarkTrackingCpuImage"
+
+# Input image. (Image)
+input_stream: "IMAGE:image"
+
+# Max number of hands to detect/track. (int)
+input_side_packet: "NUM_HANDS:num_hands"
+
+# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
+# well as inference latency generally go up with the model complexity. If
+# unspecified, functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of hands detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_hand_landmarks"
+
+# Collection of detected/predicted hand world landmarks.
+# (std::vector<LandmarkList>)
+#
+# World landmarks are real-world 3D coordinates in meters with the origin in the
+# center of the hand bounding box calculated from the landmarks.
+#
+# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
+# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
+# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
+# the 3D object itself.
+output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
+
+# Collection of handedness of the detected hands (i.e. is hand left or right),
+# each represented as a ClassificationList proto with a single Classification
+# entry. (std::vector<ClassificationList>)
+# Note that handedness is determined assuming the input image is mirrored,
+# i.e., taken with a front-facing/selfie camera with images flipped
+# horizontally.
+output_stream: "HANDEDNESS:multi_handedness"
+
+# Extra outputs (for debugging, for instance).
+# Detected palms. (std::vector<Detection>)
+output_stream: "PALM_DETECTIONS:palm_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:multi_hand_landmarks"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Converts Image to ImageFrame for HandLandmarkTrackingCpu to consume.
+node {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "IMAGE_CPU:raw_image_frame"
+  output_stream: "SOURCE_ON_GPU:is_gpu_image"
+}
+
+# TODO: Remove the extra flipping once adopting MlImage.
+# If the source images are on gpu, flip the data vertically before sending them
+# into HandLandmarkTrackingCpu. This maybe needed because OpenGL represents
+# images assuming the image origin is at the bottom-left corner, whereas
+# MediaPipe in general assumes the image origin is at the top-left corner.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:raw_image_frame"
+  input_stream: "FLIP_VERTICALLY:is_gpu_image"
+  output_stream: "IMAGE:image_frame"
+}
+
+node {
+  calculator: "HandLandmarkTrackingCpu"
+  input_stream: "IMAGE:image_frame"
+  input_side_packet: "NUM_HANDS:num_hands"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+  output_stream: "LANDMARKS:multi_hand_landmarks"
+  output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
+  output_stream: "HANDEDNESS:multi_handedness"
+  output_stream: "PALM_DETECTIONS:palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt
new file mode 100644
index 0000000..da76f4a
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt
@@ -0,0 +1,272 @@
+# MediaPipe graph to detect/predict hand landmarks on GPU.
+#
+# The procedure is done in two steps:
+# - locate palms/hands
+# - detect landmarks for each palm/hand.
+# This graph tries to skip palm detection as much as possible by reusing
+# previously detected/predicted landmarks for new images.
+
+type: "HandLandmarkTrackingGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Max number of hands to detect/track. (int)
+input_side_packet: "NUM_HANDS:num_hands"
+
+# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
+# well as inference latency generally go up with the model complexity. If
+# unspecified, functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of hands detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_hand_landmarks"
+
+# Collection of detected/predicted hand world landmarks.
+# (std::vector<LandmarkList>)
+#
+# World landmarks are real-world 3D coordinates in meters with the origin in the
+# center of the hand bounding box calculated from the landmarks.
+#
+# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
+# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
+# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
+# the 3D object itself.
+output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
+
+# Collection of handedness of the detected hands (i.e. is hand left or right),
+# each represented as a ClassificationList proto with a single Classification
+# entry. (std::vector<ClassificationList>)
+# Note that handedness is determined assuming the input image is mirrored,
+# i.e., taken with a front-facing/selfie camera with images flipped
+# horizontally.
+output_stream: "HANDEDNESS:multi_handedness"
+
+# Extra outputs (for debugging, for instance).
+# Detected palms. (std::vector<Detection>)
+output_stream: "PALM_DETECTIONS:palm_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_hand_rects_from_landmarks"
+  output_stream: "gated_prev_hand_rects_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided num_hands.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:gated_prev_hand_rects_from_landmarks"
+  input_side_packet: "num_hands"
+  output_stream: "prev_has_enough_hands"
+}
+
+# Drops the incoming image if enough hands have already been identified from the
+# previous image. Otherwise, passes the incoming image through to trigger a new
+# round of palm detection.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_hands"
+  output_stream: "palm_detection_image"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects palms.
+node {
+  calculator: "PalmDetectionGpu"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_stream: "IMAGE:palm_detection_image"
+  output_stream: "DETECTIONS:all_palm_detections"
+}
+
+# Makes sure there are no more detections than provided num_hands.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "all_palm_detections"
+  output_stream: "palm_detections"
+  input_side_packet: "num_hands"
+}
+
+# Extracts image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:palm_detection_image"
+  output_stream: "SIZE:palm_detection_image_size"
+}
+
+# Outputs each element of palm_detections at a fake timestamp for the rest of
+# the graph to process. Clones the image_size packet for each palm_detection at
+# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
+# for downstream calculators to inform them that all elements in the vector have
+# been processed.
+node {
+  calculator: "BeginLoopDetectionCalculator"
+  input_stream: "ITERABLE:palm_detections"
+  input_stream: "CLONE:palm_detection_image_size"
+  output_stream: "ITEM:palm_detection"
+  output_stream: "CLONE:image_size_for_palms"
+  output_stream: "BATCH_END:palm_detections_timestamp"
+}
+
+# Calculates region of interest (ROI) base on the specified palm.
+node {
+  calculator: "PalmDetectionDetectionToRoi"
+  input_stream: "DETECTION:palm_detection"
+  input_stream: "IMAGE_SIZE:image_size_for_palms"
+  output_stream: "ROI:hand_rect_from_palm_detection"
+}
+
+# Collects a NormalizedRect for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  name: "EndLoopForPalmDetections"
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:hand_rect_from_palm_detection"
+  input_stream: "BATCH_END:palm_detections_timestamp"
+  output_stream: "ITERABLE:hand_rects_from_palm_detections"
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on palm detections from the current image. This
+# calculator ensures that the output hand_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "hand_rects_from_palm_detections"
+  input_stream: "gated_prev_hand_rects_from_landmarks"
+  output_stream: "hand_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.5
+    }
+  }
+}
+
+# Extracts image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Outputs each element of hand_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_hand_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:hand_rects"
+  input_stream: "CLONE:0:image"
+  input_stream: "CLONE:1:image_size"
+  output_stream: "ITEM:single_hand_rect"
+  output_stream: "CLONE:0:image_for_landmarks"
+  output_stream: "CLONE:1:image_size_for_landmarks"
+  output_stream: "BATCH_END:hand_rects_timestamp"
+}
+
+# Detect hand landmarks for the specific hand rect.
+node {
+  calculator: "HandLandmarkGpu"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_stream: "IMAGE:image_for_landmarks"
+  input_stream: "ROI:single_hand_rect"
+  output_stream: "LANDMARKS:single_hand_landmarks"
+  output_stream: "WORLD_LANDMARKS:single_hand_world_landmarks"
+  output_stream: "HANDEDNESS:single_handedness"
+}
+
+# Collects the handedness for each single hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs a vector of ClassificationList at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopClassificationListCalculator"
+  input_stream: "ITEM:single_handedness"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:multi_handedness"
+}
+
+# Calculate region of interest (ROI) based on detected hand landmarks to reuse
+# on the subsequent runs of the graph.
+node {
+  calculator: "HandLandmarkLandmarksToRoi"
+  input_stream: "IMAGE_SIZE:image_size_for_landmarks"
+  input_stream: "LANDMARKS:single_hand_landmarks"
+  output_stream: "ROI:single_hand_rect_from_landmarks"
+}
+
+# Collects a set of landmarks for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:single_hand_landmarks"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:multi_hand_landmarks"
+}
+
+# Collects a set of world landmarks for each hand into a vector. Upon receiving
+# the BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopLandmarkListVectorCalculator"
+  input_stream: "ITEM:single_hand_world_landmarks"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:multi_hand_world_landmarks"
+}
+
+# Collects a NormalizedRect for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedRectCalculator"
+  input_stream: "ITEM:single_hand_rect_from_landmarks"
+  input_stream: "BATCH_END:hand_rects_timestamp"
+  output_stream: "ITERABLE:hand_rects_from_landmarks"
+}
+
+# Caches hand rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# hand rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:hand_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_hand_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu_image.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu_image.pbtxt
new file mode 100644
index 0000000..8b8e466
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu_image.pbtxt
@@ -0,0 +1,115 @@
+# MediaPipe graph to detect/predict hand landmarks on GPU.
+#
+# The procedure is done in two steps:
+# - locate palms/hands
+# - detect landmarks for each palm/hand.
+# This graph tries to skip palm detection as much as possible by reusing
+# previously detected/predicted landmarks for new images.
+
+type: "HandLandmarkTrackingGpuImage"
+
+# Input image. (Image)
+input_stream: "IMAGE:image"
+
+# Max number of hands to detect/track. (int)
+input_side_packet: "NUM_HANDS:num_hands"
+
+# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
+# well as inference latency generally go up with the model complexity. If
+# unspecified, functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+# NOTE: there will not be an output packet in the LANDMARKS stream for this
+# particular timestamp if none of hands detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:multi_hand_landmarks"
+
+# Collection of detected/predicted hand world landmarks.
+# (std::vector<LandmarkList>)
+#
+# World landmarks are real-world 3D coordinates in meters with the origin in the
+# center of the hand bounding box calculated from the landmarks.
+#
+# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
+# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
+# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
+# the 3D object itself.
+output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
+
+# Collection of handedness of the detected hands (i.e. is hand left or right),
+# each represented as a ClassificationList proto with a single Classification
+# entry. (std::vector<ClassificationList>)
+# Note that handedness is determined assuming the input image is mirrored,
+# i.e., taken with a front-facing/selfie camera with images flipped
+# horizontally.
+output_stream: "HANDEDNESS:multi_handedness"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+# Extra outputs (for debugging, for instance).
+# Detected palms. (std::vector<Detection>)
+output_stream: "PALM_DETECTIONS:palm_detections"
+# Regions of interest calculated based on landmarks.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
+# Regions of interest calculated based on palm detections.
+# (std::vector<NormalizedRect>)
+output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:multi_hand_landmarks"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Converts Image to GpuBuffer for HandLandmarkTrackingGpu to consume.
+node {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "IMAGE_GPU:raw_gpu_buffer"
+  output_stream: "SOURCE_ON_GPU:is_gpu_image"
+}
+
+# TODO: Remove the extra flipping once adopting MlImage.
+# If the source images are on gpu, flip the data vertically before sending them
+# into HandLandmarkTrackingGpu. This maybe needed because OpenGL represents
+# images assuming the image origin is at the bottom-left corner, whereas
+# MediaPipe in general assumes the image origin is at the top-left corner.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:raw_gpu_buffer"
+  input_stream: "FLIP_VERTICALLY:is_gpu_image"
+  output_stream: "IMAGE_GPU:gpu_buffer"
+}
+
+node {
+  calculator: "HandLandmarkTrackingGpu"
+  input_stream: "IMAGE:gpu_buffer"
+  input_side_packet: "NUM_HANDS:num_hands"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+  output_stream: "LANDMARKS:multi_hand_landmarks"
+  output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
+  output_stream: "HANDEDNESS:multi_handedness"
+  output_stream: "PALM_DETECTIONS:palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
+}
diff --git a/mediapipe/modules/hand_landmark/handedness.txt b/mediapipe/modules/hand_landmark/handedness.txt
new file mode 100644
index 0000000..9f636db
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/handedness.txt
@@ -0,0 +1,2 @@
+Left
+Right
diff --git a/mediapipe/modules/hand_landmark/palm_detection_detection_to_roi.pbtxt b/mediapipe/modules/hand_landmark/palm_detection_detection_to_roi.pbtxt
new file mode 100644
index 0000000..838633b
--- /dev/null
+++ b/mediapipe/modules/hand_landmark/palm_detection_detection_to_roi.pbtxt
@@ -0,0 +1,47 @@
+# MediaPipe subgraph that calculates hand ROI from palm detection.
+
+type: "PalmDetectionDetectionToRoi"
+
+# Palm detection. (Detection)
+input_stream: "DETECTION:detection"
+# Frame size. (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI (region of interest) according to landmarks, represented as normalized
+# rect. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts results of palm detection into a rectangle (normalized by image size)
+# that encloses the palm and is rotated such that the line connecting center of
+# the wrist and MCP of the middle finger is aligned with the Y-axis of the
+# rectangle.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTION:detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:raw_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0  # Center of wrist.
+      rotation_vector_end_keypoint_index: 2  # MCP of middle finger.
+      rotation_vector_target_angle_degrees: 90
+    }
+  }
+}
+
+# Expands and shifts the rectangle that contains the palm so that it's likely
+# to cover the entire hand.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:raw_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 2.6
+      scale_y: 2.6
+      shift_y: -0.5
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/BUILD b/mediapipe/modules/holistic_landmark/BUILD
new file mode 100644
index 0000000..44854c0
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/BUILD
@@ -0,0 +1,267 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/tool:mediapipe_graph.bzl", "mediapipe_simple_subgraph")
+
+# TODO: revert to private.
+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])
+
+exports_files([
+    "hand_recrop.tflite",
+])
+
+mediapipe_simple_subgraph(
+    name = "face_landmarks_from_pose_gpu",
+    graph = "face_landmarks_from_pose_gpu.pbtxt",
+    register_as = "FaceLandmarksFromPoseGpu",
+    deps = [
+        ":face_detection_front_detections_to_roi",
+        ":face_landmarks_from_pose_to_recrop_roi",
+        ":face_tracking",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_by_roi_gpu",
+        "//mediapipe/modules/face_landmark:face_landmark_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmarks_from_pose_cpu",
+    graph = "face_landmarks_from_pose_cpu.pbtxt",
+    register_as = "FaceLandmarksFromPoseCpu",
+    deps = [
+        ":face_detection_front_detections_to_roi",
+        ":face_landmarks_from_pose_to_recrop_roi",
+        ":face_tracking",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/modules/face_detection:face_detection_short_range_by_roi_cpu",
+        "//mediapipe/modules/face_landmark:face_landmark_cpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmarks_to_roi",
+    graph = "face_landmarks_to_roi.pbtxt",
+    register_as = "FaceLandmarksToRoi",
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_detection_front_detections_to_roi",
+    graph = "face_detection_front_detections_to_roi.pbtxt",
+    register_as = "FaceDetectionFrontDetectionsToRoi",
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_tracking",
+    graph = "face_tracking.pbtxt",
+    register_as = "FaceTracking",
+    deps = [
+        ":face_landmarks_to_roi",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/modules/holistic_landmark/calculators:roi_tracking_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "face_landmarks_from_pose_to_recrop_roi",
+    graph = "face_landmarks_from_pose_to_recrop_roi.pbtxt",
+    register_as = "FaceLandmarksFromPoseToRecropRoi",
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmarks_from_pose_gpu",
+    graph = "hand_landmarks_from_pose_gpu.pbtxt",
+    register_as = "HandLandmarksFromPoseGpu",
+    deps = [
+        ":hand_landmarks_from_pose_to_recrop_roi",
+        ":hand_recrop_by_roi_gpu",
+        ":hand_tracking",
+        ":hand_visibility_from_hand_landmarks_from_pose",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/modules/hand_landmark:hand_landmark_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmarks_from_pose_cpu",
+    graph = "hand_landmarks_from_pose_cpu.pbtxt",
+    register_as = "HandLandmarksFromPoseCpu",
+    deps = [
+        ":hand_landmarks_from_pose_to_recrop_roi",
+        ":hand_recrop_by_roi_cpu",
+        ":hand_tracking",
+        ":hand_visibility_from_hand_landmarks_from_pose",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/modules/hand_landmark:hand_landmark_cpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmarks_to_roi",
+    graph = "hand_landmarks_to_roi.pbtxt",
+    register_as = "HandLandmarksToRoi",
+    deps = [
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+        "//mediapipe/modules/hand_landmark/calculators:hand_landmarks_to_rect_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_recrop_by_roi_gpu",
+    graph = "hand_recrop_by_roi_gpu.pbtxt",
+    register_as = "HandRecropByRoiGpu",
+    deps = [
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_recrop_by_roi_cpu",
+    graph = "hand_recrop_by_roi_cpu.pbtxt",
+    register_as = "HandRecropByRoiCpu",
+    deps = [
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_tracking",
+    graph = "hand_tracking.pbtxt",
+    register_as = "HandTracking",
+    deps = [
+        ":hand_landmarks_to_roi",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/modules/holistic_landmark/calculators:roi_tracking_calculator",
+    ],
+)
+
+# TODO: parametrize holistic_landmark graph with visibility and make private.
+mediapipe_simple_subgraph(
+    name = "hand_wrist_for_pose",
+    graph = "hand_wrist_for_pose.pbtxt",
+    register_as = "HandWristForPose",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/util:set_landmark_visibility_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmarks_left_and_right_gpu",
+    graph = "hand_landmarks_left_and_right_gpu.pbtxt",
+    register_as = "HandLandmarksLeftAndRightGpu",
+    deps = [
+        ":hand_landmarks_from_pose_gpu",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmarks_left_and_right_cpu",
+    graph = "hand_landmarks_left_and_right_cpu.pbtxt",
+    register_as = "HandLandmarksLeftAndRightCpu",
+    deps = [
+        ":hand_landmarks_from_pose_cpu",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_landmarks_from_pose_to_recrop_roi",
+    graph = "hand_landmarks_from_pose_to_recrop_roi.pbtxt",
+    register_as = "HandLandmarksFromPoseToRecropRoi",
+    deps = [
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+        "//mediapipe/modules/holistic_landmark/calculators:hand_detections_from_pose_to_rects_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "hand_visibility_from_hand_landmarks_from_pose",
+    graph = "hand_visibility_from_hand_landmarks_from_pose.pbtxt",
+    register_as = "HandVisibilityFromHandLandmarksFromPose",
+    deps = [
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/util:landmark_visibility_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "holistic_landmark_gpu",
+    graph = "holistic_landmark_gpu.pbtxt",
+    register_as = "HolisticLandmarkGpu",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":face_landmarks_from_pose_gpu",
+        ":hand_landmarks_left_and_right_gpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/modules/pose_landmark:pose_landmark_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "holistic_landmark_cpu",
+    graph = "holistic_landmark_cpu.pbtxt",
+    register_as = "HolisticLandmarkCpu",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":face_landmarks_from_pose_cpu",
+        ":hand_landmarks_left_and_right_cpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/modules/pose_landmark:pose_landmark_cpu",
+    ],
+)
diff --git a/mediapipe/modules/holistic_landmark/README.md b/mediapipe/modules/holistic_landmark/README.md
new file mode 100644
index 0000000..d285f15
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/README.md
@@ -0,0 +1,6 @@
+# holistic_landmark
+
+Subgraphs|Details
+:--- | :---
+[`HolisticLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt)| Predicts pose + left/right hand + face landmarks. (CPU input)
+[`HolisticLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt)| Predicts pose + left/right hand + face landmarks. (GPU input.)
diff --git a/mediapipe/modules/holistic_landmark/calculators/BUILD b/mediapipe/modules/holistic_landmark/calculators/BUILD
new file mode 100644
index 0000000..c3c0919
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/calculators/BUILD
@@ -0,0 +1,63 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "hand_detections_from_pose_to_rects_calculator",
+    srcs = ["hand_detections_from_pose_to_rects_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:detections_to_rects_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_options_cc_proto",
+        "//mediapipe/framework/formats:detection_cc_proto",
+        "//mediapipe/framework/formats:location_data_cc_proto",
+        "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ],
+    alwayslink = 1,
+)
+
+mediapipe_proto_library(
+    name = "roi_tracking_calculator_proto",
+    srcs = ["roi_tracking_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+cc_library(
+    name = "roi_tracking_calculator",
+    srcs = ["roi_tracking_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":roi_tracking_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:rectangle",
+        "@com_google_absl//absl/strings:str_format",
+    ],
+    alwayslink = 1,
+)
diff --git a/mediapipe/modules/holistic_landmark/calculators/hand_detections_from_pose_to_rects_calculator.cc b/mediapipe/modules/holistic_landmark/calculators/hand_detections_from_pose_to_rects_calculator.cc
new file mode 100644
index 0000000..5afdb8a
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/calculators/hand_detections_from_pose_to_rects_calculator.cc
@@ -0,0 +1,156 @@
+#include <cmath>
+
+#include "mediapipe/calculators/util/detections_to_rects_calculator.h"
+#include "mediapipe/calculators/util/detections_to_rects_calculator.pb.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_options.pb.h"
+#include "mediapipe/framework/formats/detection.pb.h"
+#include "mediapipe/framework/formats/location_data.pb.h"
+#include "mediapipe/framework/formats/rect.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+
+namespace mediapipe {
+
+namespace {}  // namespace
+
+// Generates a hand ROI based on a hand detection derived from hand-related pose
+// landmarks.
+//
+// Inputs:
+//   DETECTION - Detection.
+//     Detection to convert to ROI. Must contain 3 key points indicating: wrist,
+//     pinky and index fingers.
+//
+//   IMAGE_SIZE - std::pair<int, int>
+//     Image width and height.
+//
+// Outputs:
+//   NORM_RECT - NormalizedRect.
+//     ROI based on passed input.
+//
+// Examples
+// node {
+//   calculator: "HandDetectionsFromPoseToRectsCalculator"
+//   input_stream: "DETECTION:hand_detection_from_pose"
+//   input_stream: "IMAGE_SIZE:image_size"
+//   output_stream: "NORM_RECT:hand_roi_from_pose"
+// }
+class HandDetectionsFromPoseToRectsCalculator
+    : public DetectionsToRectsCalculator {
+ public:
+  absl::Status Open(CalculatorContext* cc) override;
+
+ private:
+  ::absl::Status DetectionToNormalizedRect(const Detection& detection,
+                                           const DetectionSpec& detection_spec,
+                                           NormalizedRect* rect) override;
+  absl::Status ComputeRotation(const Detection& detection,
+                               const DetectionSpec& detection_spec,
+                               float* rotation) override;
+};
+REGISTER_CALCULATOR(HandDetectionsFromPoseToRectsCalculator);
+
+namespace {
+
+constexpr int kWrist = 0;
+constexpr int kPinky = 1;
+constexpr int kIndex = 2;
+
+constexpr char kImageSizeTag[] = "IMAGE_SIZE";
+
+}  // namespace
+
+::absl::Status HandDetectionsFromPoseToRectsCalculator::Open(
+    CalculatorContext* cc) {
+  RET_CHECK(cc->Inputs().HasTag(kImageSizeTag))
+      << "Image size is required to calculate rotated rect.";
+  cc->SetOffset(TimestampDiff(0));
+  target_angle_ = M_PI * 0.5f;
+  rotate_ = true;
+  options_ = cc->Options<DetectionsToRectsCalculatorOptions>();
+  output_zero_rect_for_empty_detections_ =
+      options_.output_zero_rect_for_empty_detections();
+
+  return ::absl::OkStatus();
+}
+
+::absl::Status
+HandDetectionsFromPoseToRectsCalculator ::DetectionToNormalizedRect(
+    const Detection& detection, const DetectionSpec& detection_spec,
+    NormalizedRect* rect) {
+  const auto& location_data = detection.location_data();
+  const auto& image_size = detection_spec.image_size;
+  RET_CHECK(image_size) << "Image size is required to calculate rotation";
+
+  const float x_wrist =
+      location_data.relative_keypoints(kWrist).x() * image_size->first;
+  const float y_wrist =
+      location_data.relative_keypoints(kWrist).y() * image_size->second;
+
+  const float x_index =
+      location_data.relative_keypoints(kIndex).x() * image_size->first;
+  const float y_index =
+      location_data.relative_keypoints(kIndex).y() * image_size->second;
+
+  const float x_pinky =
+      location_data.relative_keypoints(kPinky).x() * image_size->first;
+  const float y_pinky =
+      location_data.relative_keypoints(kPinky).y() * image_size->second;
+
+  // Estimate middle finger.
+  const float x_middle = (2.f * x_index + x_pinky) / 3.f;
+  const float y_middle = (2.f * y_index + y_pinky) / 3.f;
+
+  // Crop center as middle finger.
+  const float center_x = x_middle;
+  const float center_y = y_middle;
+
+  // Bounding box size as double distance from middle finger to wrist.
+  const float box_size =
+      std::sqrt((x_middle - x_wrist) * (x_middle - x_wrist) +
+                (y_middle - y_wrist) * (y_middle - y_wrist)) *
+      2.0;
+
+  // Set resulting bounding box.
+  rect->set_x_center(center_x / image_size->first);
+  rect->set_y_center(center_y / image_size->second);
+  rect->set_width(box_size / image_size->first);
+  rect->set_height(box_size / image_size->second);
+
+  return ::absl::OkStatus();
+}
+
+absl::Status HandDetectionsFromPoseToRectsCalculator::ComputeRotation(
+    const Detection& detection, const DetectionSpec& detection_spec,
+    float* rotation) {
+  const auto& location_data = detection.location_data();
+  const auto& image_size = detection_spec.image_size;
+  RET_CHECK(image_size) << "Image size is required to calculate rotation";
+
+  const float x_wrist =
+      location_data.relative_keypoints(kWrist).x() * image_size->first;
+  const float y_wrist =
+      location_data.relative_keypoints(kWrist).y() * image_size->second;
+
+  const float x_index =
+      location_data.relative_keypoints(kIndex).x() * image_size->first;
+  const float y_index =
+      location_data.relative_keypoints(kIndex).y() * image_size->second;
+
+  const float x_pinky =
+      location_data.relative_keypoints(kPinky).x() * image_size->first;
+  const float y_pinky =
+      location_data.relative_keypoints(kPinky).y() * image_size->second;
+
+  // Estimate middle finger.
+  const float x_middle = (2.f * x_index + x_pinky) / 3.f;
+  const float y_middle = (2.f * y_index + y_pinky) / 3.f;
+
+  *rotation = NormalizeRadians(
+      target_angle_ - std::atan2(-(y_middle - y_wrist), x_middle - x_wrist));
+
+  return ::absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.cc b/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.cc
new file mode 100644
index 0000000..0da6cd7
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.cc
@@ -0,0 +1,358 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <math.h>
+
+#include <cstdlib>
+
+#include "absl/strings/str_format.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/formats/rect.pb.h"
+#include "mediapipe/framework/port/logging.h"
+#include "mediapipe/framework/port/rectangle.h"
+#include "mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kPrevLandmarksTag[] = "PREV_LANDMARKS";
+constexpr char kPrevLandmarksRectTag[] = "PREV_LANDMARKS_RECT";
+constexpr char kRecropRectTag[] = "RECROP_RECT";
+constexpr char kImageSizeTag[] = "IMAGE_SIZE";
+constexpr char kTrackingRectTag[] = "TRACKING_RECT";
+
+// TODO: Use rect rotation.
+// Verifies that Intersection over Union of previous frame rect and current
+// frame re-crop rect is less than threshold.
+bool IouRequirementsSatisfied(const NormalizedRect& prev_rect,
+                              const NormalizedRect& recrop_rect,
+                              const std::pair<int, int>& image_size,
+                              const float min_iou) {
+  auto r1 = Rectangle_f(prev_rect.x_center() * image_size.first,
+                        prev_rect.y_center() * image_size.second,
+                        prev_rect.width() * image_size.first,
+                        prev_rect.height() * image_size.second);
+  auto r2 = Rectangle_f(recrop_rect.x_center() * image_size.first,
+                        recrop_rect.y_center() * image_size.second,
+                        recrop_rect.width() * image_size.first,
+                        recrop_rect.height() * image_size.second);
+
+  const float intersection_area = r1.Intersect(r2).Area();
+  const float union_area = r1.Area() + r2.Area() - intersection_area;
+
+  const float intersection_threshold = union_area * min_iou;
+  if (intersection_area < intersection_threshold) {
+    VLOG(1) << absl::StrFormat("Lost tracking: IoU intersection %f < %f",
+                               intersection_area, intersection_threshold);
+    return false;
+  }
+
+  return true;
+}
+
+// Verifies that current frame re-crop rect rotation/translation/scale didn't
+// change much comparing to the previous frame rect. Translation and scale are
+// normalized by current frame re-crop rect.
+bool RectRequirementsSatisfied(const NormalizedRect& prev_rect,
+                               const NormalizedRect& recrop_rect,
+                               const std::pair<int, int> image_size,
+                               const float rotation_degrees,
+                               const float translation, const float scale) {
+  // Rotate both rects so that re-crop rect edges are parallel to XY axes. That
+  // will allow to compute x/y translation of the previous frame rect along axes
+  // of the current frame re-crop rect.
+  const float rotation = -recrop_rect.rotation();
+
+  const float cosa = cos(rotation);
+  const float sina = sin(rotation);
+
+  // Rotate previous frame rect and get its parameters.
+  const float prev_rect_x = prev_rect.x_center() * image_size.first * cosa -
+                            prev_rect.y_center() * image_size.second * sina;
+  const float prev_rect_y = prev_rect.x_center() * image_size.first * sina +
+                            prev_rect.y_center() * image_size.second * cosa;
+  const float prev_rect_width = prev_rect.width() * image_size.first;
+  const float prev_rect_height = prev_rect.height() * image_size.second;
+  const float prev_rect_rotation = prev_rect.rotation() / M_PI * 180.f;
+
+  // Rotate current frame re-crop rect and get its parameters.
+  const float recrop_rect_x = recrop_rect.x_center() * image_size.first * cosa -
+                              recrop_rect.y_center() * image_size.second * sina;
+  const float recrop_rect_y = recrop_rect.x_center() * image_size.first * sina +
+                              recrop_rect.y_center() * image_size.second * cosa;
+  const float recrop_rect_width = recrop_rect.width() * image_size.first;
+  const float recrop_rect_height = recrop_rect.height() * image_size.second;
+  const float recrop_rect_rotation = recrop_rect.rotation() / M_PI * 180.f;
+
+  // Rect requirements are satisfied unless one of the checks below fails.
+  bool satisfied = true;
+
+  // Ensure that rotation diff is in [0, 180] range.
+  float rotation_diff = prev_rect_rotation - recrop_rect_rotation;
+  if (rotation_diff > 180.f) {
+    rotation_diff -= 360.f;
+  }
+  if (rotation_diff < -180.f) {
+    rotation_diff += 360.f;
+  }
+  rotation_diff = abs(rotation_diff);
+  if (rotation_diff > rotation_degrees) {
+    satisfied = false;
+    VLOG(1) << absl::StrFormat("Lost tracking: rect rotation %f > %f",
+                               rotation_diff, rotation_degrees);
+  }
+
+  const float x_diff = abs(prev_rect_x - recrop_rect_x);
+  const float x_threshold = recrop_rect_width * translation;
+  if (x_diff > x_threshold) {
+    satisfied = false;
+    VLOG(1) << absl::StrFormat("Lost tracking: rect x translation %f > %f",
+                               x_diff, x_threshold);
+  }
+
+  const float y_diff = abs(prev_rect_y - recrop_rect_y);
+  const float y_threshold = recrop_rect_height * translation;
+  if (y_diff > y_threshold) {
+    satisfied = false;
+    VLOG(1) << absl::StrFormat("Lost tracking: rect y translation %f > %f",
+                               y_diff, y_threshold);
+  }
+
+  const float width_diff = abs(prev_rect_width - recrop_rect_width);
+  const float width_threshold = recrop_rect_width * scale;
+  if (width_diff > width_threshold) {
+    satisfied = false;
+    VLOG(1) << absl::StrFormat("Lost tracking: rect width %f > %f", width_diff,
+                               width_threshold);
+  }
+
+  const float height_diff = abs(prev_rect_height - recrop_rect_height);
+  const float height_threshold = recrop_rect_height * scale;
+  if (height_diff > height_threshold) {
+    satisfied = false;
+    VLOG(1) << absl::StrFormat("Lost tracking: rect height %f > %f",
+                               height_diff, height_threshold);
+  }
+
+  return satisfied;
+}
+
+// Verifies that landmarks from the previous frame are within re-crop rectangle
+// bounds on the current frame.
+bool LandmarksRequirementsSatisfied(const NormalizedLandmarkList& landmarks,
+                                    const NormalizedRect& recrop_rect,
+                                    const std::pair<int, int> image_size,
+                                    const float recrop_rect_margin) {
+  // Rotate both re-crop rectangle and landmarks so that re-crop rectangle edges
+  // are parallel to XY axes. It will allow to easily check if landmarks are
+  // within re-crop rect bounds along re-crop rect axes.
+  //
+  // Rect rotation is specified clockwise. To apply cos/sin functions we
+  // transform it into counterclockwise.
+  const float rotation = -recrop_rect.rotation();
+
+  const float cosa = cos(rotation);
+  const float sina = sin(rotation);
+
+  // Rotate rect.
+  const float rect_x = recrop_rect.x_center() * image_size.first * cosa -
+                       recrop_rect.y_center() * image_size.second * sina;
+  const float rect_y = recrop_rect.x_center() * image_size.first * sina +
+                       recrop_rect.y_center() * image_size.second * cosa;
+  const float rect_width =
+      recrop_rect.width() * image_size.first * (1.f + recrop_rect_margin);
+  const float rect_height =
+      recrop_rect.height() * image_size.second * (1.f + recrop_rect_margin);
+
+  // Get rect bounds.
+  const float rect_left = rect_x - rect_width * 0.5f;
+  const float rect_right = rect_x + rect_width * 0.5f;
+  const float rect_top = rect_y - rect_height * 0.5f;
+  const float rect_bottom = rect_y + rect_height * 0.5f;
+
+  for (int i = 0; i < landmarks.landmark_size(); ++i) {
+    const auto& landmark = landmarks.landmark(i);
+    const float x = landmark.x() * image_size.first * cosa -
+                    landmark.y() * image_size.second * sina;
+    const float y = landmark.x() * image_size.first * sina +
+                    landmark.y() * image_size.second * cosa;
+
+    if (!(rect_left < x && x < rect_right && rect_top < y && y < rect_bottom)) {
+      VLOG(1) << "Lost tracking: landmarks out of re-crop rect";
+      return false;
+    }
+  }
+
+  return true;
+}
+
+}  // namespace
+
+// A calculator to track object rectangle between frames.
+//
+// Calculator checks that all requirements for tracking are satisfied and uses
+// rectangle from the previous frame in this case, otherwise - uses current
+// frame re-crop rectangle.
+//
+// There are several types of tracking requirements that can be configured via
+// options:
+//   IoU: Verifies that IoU of the previous frame rectangle and current frame
+//     re-crop rectangle is less than a given threshold.
+//   Rect parameters: Verifies that rotation/translation/scale of the re-crop
+//     rectangle on the current frame is close to the rectangle from the
+//     previous frame within given thresholds.
+//   Landmarks: Verifies that landmarks from the previous frame are within
+//     the re-crop rectangle on the current frame.
+//
+// Inputs:
+//   PREV_LANDMARKS: Object landmarks from the previous frame.
+//   PREV_LANDMARKS_RECT: Object rectangle based on the landmarks from the
+//     previous frame.
+//   RECROP_RECT: Object re-crop rectangle from the current frame.
+//   IMAGE_SIZE: Image size to transform normalized coordinates to absolute.
+//
+// Outputs:
+//   TRACKING_RECT: Rectangle to use for object prediction on the current frame.
+//     It will be either object rectangle from the previous frame (if all
+//     tracking requirements are satisfied) or re-crop rectangle from the
+//     current frame (if tracking lost the object).
+//
+// Example config:
+//   node {
+//     calculator: "RoiTrackingCalculator"
+//     input_stream: "PREV_LANDMARKS:prev_hand_landmarks"
+//     input_stream: "PREV_LANDMARKS_RECT:prev_hand_landmarks_rect"
+//     input_stream: "RECROP_RECT:hand_recrop_rect"
+//     input_stream: "IMAGE_SIZE:image_size"
+//     output_stream: "TRACKING_RECT:hand_tracking_rect"
+//     options: {
+//       [mediapipe.RoiTrackingCalculatorOptions.ext] {
+//         rect_requirements: {
+//           rotation_degrees: 40.0
+//           translation: 0.2
+//           scale: 0.4
+//         }
+//         landmarks_requirements: {
+//           recrop_rect_margin: -0.1
+//         }
+//       }
+//     }
+//   }
+class RoiTrackingCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  RoiTrackingCalculatorOptions options_;
+};
+REGISTER_CALCULATOR(RoiTrackingCalculator);
+
+absl::Status RoiTrackingCalculator::GetContract(CalculatorContract* cc) {
+  cc->Inputs().Tag(kPrevLandmarksTag).Set<NormalizedLandmarkList>();
+  cc->Inputs().Tag(kPrevLandmarksRectTag).Set<NormalizedRect>();
+  cc->Inputs().Tag(kRecropRectTag).Set<NormalizedRect>();
+  cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
+  cc->Outputs().Tag(kTrackingRectTag).Set<NormalizedRect>();
+
+  return absl::OkStatus();
+}
+
+absl::Status RoiTrackingCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  options_ = cc->Options<RoiTrackingCalculatorOptions>();
+  return absl::OkStatus();
+}
+
+absl::Status RoiTrackingCalculator::Process(CalculatorContext* cc) {
+  // If there is no current frame re-crop rect (i.e. object is not present on
+  // the current frame) - return empty packet.
+  if (cc->Inputs().Tag(kRecropRectTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+
+  // If there is no previous rect, but there is current re-crop rect - return
+  // current re-crop rect as is.
+  if (cc->Inputs().Tag(kPrevLandmarksRectTag).IsEmpty()) {
+    cc->Outputs()
+        .Tag(kTrackingRectTag)
+        .AddPacket(cc->Inputs().Tag(kRecropRectTag).Value());
+    return absl::OkStatus();
+  }
+
+  // At this point we have both previous rect (which also means we have previous
+  // landmarks) and currrent re-crop rect.
+  const auto& prev_landmarks =
+      cc->Inputs().Tag(kPrevLandmarksTag).Get<NormalizedLandmarkList>();
+  const auto& prev_rect =
+      cc->Inputs().Tag(kPrevLandmarksRectTag).Get<NormalizedRect>();
+  const auto& recrop_rect =
+      cc->Inputs().Tag(kRecropRectTag).Get<NormalizedRect>();
+  const auto& image_size =
+      cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
+
+  // Keep tracking unless one of the requirements below is not satisfied.
+  bool keep_tracking = true;
+
+  // If IoU of the previous rect and current re-crop rect is lower than allowed
+  // threshold - use current re-crop rect.
+  if (options_.has_iou_requirements() &&
+      !IouRequirementsSatisfied(prev_rect, recrop_rect, image_size,
+                                options_.iou_requirements().min_iou())) {
+    keep_tracking = false;
+  }
+
+  // If previous rect and current re-crop rect differ more than it is allowed by
+  // the augmentations (used during the model training) - use current re-crop
+  // rect.
+  if (options_.has_rect_requirements() &&
+      !RectRequirementsSatisfied(
+          prev_rect, recrop_rect, image_size,
+          options_.rect_requirements().rotation_degrees(),
+          options_.rect_requirements().translation(),
+          options_.rect_requirements().scale())) {
+    keep_tracking = false;
+  }
+
+  // If landmarks from the previous frame are not in the current re-crop rect
+  // (i.e. object moved too fast and using previous frame rect won't cover
+  // landmarks on the current frame) - use current re-crop rect.
+  if (options_.has_landmarks_requirements() &&
+      !LandmarksRequirementsSatisfied(
+          prev_landmarks, recrop_rect, image_size,
+          options_.landmarks_requirements().recrop_rect_margin())) {
+    keep_tracking = false;
+  }
+
+  // If object didn't move a lot comparing to the previous frame - we'll keep
+  // tracking it and will return rect from the previous frame, otherwise -
+  // return re-crop rect from the current frame.
+  if (keep_tracking) {
+    cc->Outputs()
+        .Tag(kTrackingRectTag)
+        .AddPacket(cc->Inputs().Tag(kPrevLandmarksRectTag).Value());
+  } else {
+    cc->Outputs()
+        .Tag(kTrackingRectTag)
+        .AddPacket(cc->Inputs().Tag(kRecropRectTag).Value());
+    VLOG(1) << "Lost tracking: check messages above for details";
+  }
+
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.proto b/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.proto
new file mode 100644
index 0000000..ec3cf22
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.proto
@@ -0,0 +1,59 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message RoiTrackingCalculatorOptions {
+  extend CalculatorOptions {
+    optional RoiTrackingCalculatorOptions ext = 329994630;
+  }
+
+  // Verifies that Intersection over Union of previous frame rect and current
+  // frame re-crop rect is less than threshold.
+  message IouRequirements {
+    optional float min_iou = 1 [default = 0.5];
+  }
+
+  // Verifies that current frame re-crop rect rotation/translation/scale didn't
+  // change much comparing to the previous frame rect.
+  message RectRequirements {
+    // Allowed rotation change defined in degrees.
+    optional float rotation_degrees = 1 [default = 10.0];
+
+    // Allowed translation change defined as absolute translation normalized by
+    // re-crop rectangle size.
+    optional float translation = 2 [default = 0.1];
+
+    // Allowed scale change defined as absolute translation normalized by
+    // re-crop rectangle size.
+    optional float scale = 3 [default = 0.1];
+  }
+
+  // Verifies that landmarks from the previous frame are within re-crop
+  // rectangle bounds on the current frame.
+  message LandmarksRequirements {
+    // Margin to apply to re-crop rectangle before checking verifing landmarks.
+    optional float recrop_rect_margin = 1 [default = 0.0];
+  }
+
+  optional IouRequirements iou_requirements = 1;
+
+  optional RectRequirements rect_requirements = 2;
+
+  optional LandmarksRequirements landmarks_requirements = 3;
+}
diff --git a/mediapipe/modules/holistic_landmark/face_detection_front_detections_to_roi.pbtxt b/mediapipe/modules/holistic_landmark/face_detection_front_detections_to_roi.pbtxt
new file mode 100644
index 0000000..7d9fa9e
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/face_detection_front_detections_to_roi.pbtxt
@@ -0,0 +1,48 @@
+# Calculates ROI from detections provided by `face_detection_short_range.tflite`
+# model.
+type: "FaceDetectionFrontDetectionsToRoi"
+
+# Detected faces. (std::vector<Detection>)
+input_stream: "DETECTIONS:detections"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# Refined (more accurate) ROI to use for face landmarks prediction.
+# (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts the face detection into a rectangle (normalized by image size)
+# that encloses the face and is rotated such that the line connecting right side
+# of the right eye and left side of the left eye is aligned with the X-axis of
+# the rectangle.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTIONS:detections"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:raw_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0  # Right eye.
+      rotation_vector_end_keypoint_index: 1  # Left eye.
+      rotation_vector_target_angle_degrees: 0
+      conversion_mode: USE_KEYPOINTS
+    }
+  }
+}
+
+# Expands and shifts the rectangle that contains the face so that it's likely
+# to cover the entire face.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:raw_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 2.0
+      scale_y: 2.0
+      shift_y: -0.1
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_cpu.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_cpu.pbtxt
new file mode 100644
index 0000000..1d99672
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_cpu.pbtxt
@@ -0,0 +1,82 @@
+# Predicts face landmarks within an ROI derived from face-related pose
+# landmarks.
+
+type: "FaceLandmarksFromPoseCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# Face-related pose landmarks. (NormalizedLandmarkList)
+input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
+
+# Whether to run the face landmark model with attention on lips and eyes to
+# provide more accuracy, and additionally output iris landmarks. If unspecified,
+# functions as set to false. (bool)
+input_side_packet: "REFINE_LANDMARKS:refine_landmarks"
+
+# Face landmarks. (NormalizedLandmarkList)
+output_stream: "FACE_LANDMARKS:face_landmarks"
+
+# Debug outputs.
+# Face ROI derived from face-related pose landmarks, which defines the search
+# region for the face detection model. (NormalizedRect)
+output_stream: "FACE_ROI_FROM_POSE:face_roi_from_pose"
+# Refined face crop rectangle predicted by face detection model.
+# (NormalizedRect)
+output_stream: "FACE_ROI_FROM_DETECTION:face_roi_from_detection"
+# Rectangle used to predict face landmarks. (NormalizedRect)
+output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
+
+# TODO: do not predict face when most of the face landmarks from
+# pose are invisible.
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Gets ROI for re-crop model from face-related pose landmarks.
+node {
+  calculator: "FaceLandmarksFromPoseToRecropRoi"
+  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:face_roi_from_pose"
+}
+
+# Detects faces within the face ROI calculated from pose landmarks. This is done
+# to refine face ROI for further landmark detection as ROI calculated from
+# pose landmarks may be inaccurate.
+node {
+  calculator: "FaceDetectionShortRangeByRoiCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:face_roi_from_pose"
+  output_stream: "DETECTIONS:face_detections"
+}
+
+# Calculates refined face ROI.
+node {
+  calculator: "FaceDetectionFrontDetectionsToRoi"
+  input_stream: "DETECTIONS:face_detections"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:face_roi_from_detection"
+}
+
+# Gets face tracking rectangle (either face rectangle from the previous
+# frame or face re-crop rectangle from the current frame) for face prediction.
+node {
+  calculator: "FaceTracking"
+  input_stream: "LANDMARKS:face_landmarks"
+  input_stream: "FACE_RECROP_ROI:face_roi_from_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
+}
+
+# Predicts face landmarks from the tracking rectangle.
+node {
+  calculator: "FaceLandmarkCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:face_tracking_roi"
+  input_side_packet: "WITH_ATTENTION:refine_landmarks"
+  output_stream: "LANDMARKS:face_landmarks"
+}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_gpu.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_gpu.pbtxt
new file mode 100644
index 0000000..24a9854
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_gpu.pbtxt
@@ -0,0 +1,82 @@
+# Predicts face landmarks within an ROI derived from face-related pose
+# landmarks.
+
+type: "FaceLandmarksFromPoseGpu"
+
+# GPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# Face-related pose landmarks. (NormalizedLandmarkList)
+input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
+
+# Whether to run the face landmark model with attention on lips and eyes to
+# provide more accuracy, and additionally output iris landmarks. If unspecified,
+# functions as set to false. (bool)
+input_side_packet: "REFINE_LANDMARKS:refine_landmarks"
+
+# Face landmarks. (NormalizedLandmarkList)
+output_stream: "FACE_LANDMARKS:face_landmarks"
+
+# Debug outputs.
+# Face ROI derived from face-related pose landmarks, which defines the search
+# region for the face detection model. (NormalizedRect)
+output_stream: "FACE_ROI_FROM_POSE:face_roi_from_pose"
+# Refined face crop rectangle predicted by face detection model.
+# (NormalizedRect)
+output_stream: "FACE_ROI_FROM_DETECTION:face_roi_from_detection"
+# Rectangle used to predict face landmarks. (NormalizedRect)
+output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
+
+# TODO: do not predict face when most of the face landmarks from
+# pose are invisible.
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Gets ROI for re-crop model from face-related pose landmarks.
+node {
+  calculator: "FaceLandmarksFromPoseToRecropRoi"
+  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:face_roi_from_pose"
+}
+
+# Detects faces within the face ROI calculated from pose landmarks. This is done
+# to refine face ROI for further landmark detection as ROI calculated from
+# pose landmarks may be inaccurate.
+node {
+  calculator: "FaceDetectionShortRangeByRoiGpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:face_roi_from_pose"
+  output_stream: "DETECTIONS:face_detections"
+}
+
+# Calculates refined face ROI.
+node {
+  calculator: "FaceDetectionFrontDetectionsToRoi"
+  input_stream: "DETECTIONS:face_detections"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:face_roi_from_detection"
+}
+
+# Gets face tracking rectangle (either face rectangle from the previous
+# frame or face re-crop rectangle from the current frame) for face prediction.
+node {
+  calculator: "FaceTracking"
+  input_stream: "LANDMARKS:face_landmarks"
+  input_stream: "FACE_RECROP_ROI:face_roi_from_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
+}
+
+# Predicts face landmarks from the tracking rectangle.
+node {
+  calculator: "FaceLandmarkGpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:face_tracking_roi"
+  input_side_packet: "WITH_ATTENTION:refine_landmarks"
+  output_stream: "LANDMARKS:face_landmarks"
+}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_to_recrop_roi.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_to_recrop_roi.pbtxt
new file mode 100644
index 0000000..65bd340
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_to_recrop_roi.pbtxt
@@ -0,0 +1,51 @@
+# Converts face-related pose landmarks to re-crop ROI.
+
+type: "FaceLandmarksFromPoseToRecropRoi"
+
+# Face-related pose landmarks (There should be 11 of them).
+# (NormalizedLandmarkList)
+input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI to be used for face detection. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts face-related pose landmarks to a detection that tightly encloses all
+# landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks_from_pose"
+  output_stream: "DETECTION:pose_face_detection"
+}
+
+# Converts face detection to a normalized face rectangle.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTION:pose_face_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:pose_face_rect"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 5  # Right eye.
+      rotation_vector_end_keypoint_index: 2  # Left eye.
+      rotation_vector_target_angle_degrees: 0
+    }
+  }
+}
+
+# Expands face rectangle so that it becomes big enough for face detector to
+# localize it accurately.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:pose_face_rect"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 3.0
+      scale_y: 3.0
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_to_roi.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_to_roi.pbtxt
new file mode 100644
index 0000000..8913cc1
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/face_landmarks_to_roi.pbtxt
@@ -0,0 +1,53 @@
+# Converts face landmarks to ROI.
+
+type: "FaceLandmarksToRoi"
+
+# Face landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:face_landmarks"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI according to landmarks. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts face landmarks to a detection that tightly encloses all landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:face_landmarks"
+  output_stream: "DETECTION:face_detection"
+}
+
+# Converts the face detection into a rectangle (normalized by image size)
+# that encloses the face and is rotated such that the line connecting center of
+# the wrist and MCP of the middle finger is aligned with the Y-axis of the
+# rectangle.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTION:face_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:face_landmarks_rect_tight"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 33  # Right side of left eye.
+      rotation_vector_end_keypoint_index: 263  # Left side of right eye.
+      rotation_vector_target_angle_degrees: 0
+    }
+  }
+}
+
+# Expands the face rectangle so that it's likely to contain the face even with
+# some motion.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:face_landmarks_rect_tight"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.5
+      scale_y: 1.5
+      # TODO: remove `square_long` where appropriat
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/face_tracking.pbtxt b/mediapipe/modules/holistic_landmark/face_tracking.pbtxt
new file mode 100644
index 0000000..53022d3
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/face_tracking.pbtxt
@@ -0,0 +1,61 @@
+# Decides what ROI to use for face landmarks prediction: either previous frame
+# landmarks ROI or the current frame face re-crop ROI.
+
+type: "FaceTracking"
+
+# Face landmarks from the current frame. They will be memorized for tracking on
+# the next frame. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:face_landmarks"
+# Face re-crop ROI from the current frame. (NormalizedRect)
+input_stream: "FACE_RECROP_ROI:face_recrop_roi"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# Face tracking ROI. Which is either face landmarks ROI from the previous frame
+# if face is still tracked, or face re-crop ROI from the current frame
+# otherwise. (NormalizedRect)
+output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
+
+# Keeps track of face landmarks from the previous frame.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image_size"
+  input_stream: "LOOP:face_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_face_landmarks"
+}
+
+# Gets hand landarmsk rect.
+node {
+  calculator: "FaceLandmarksToRoi"
+  input_stream: "LANDMARKS:prev_face_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:prev_face_landmarks_rect"
+}
+
+# Checks that all requirements for tracking are satisfied and use face rectangle
+# from the previous frame in that case. Otherwise - use face re-crop rectangle
+# from the current frame.
+node {
+  calculator: "RoiTrackingCalculator"
+  input_stream: "PREV_LANDMARKS:prev_face_landmarks"
+  input_stream: "PREV_LANDMARKS_RECT:prev_face_landmarks_rect"
+  input_stream: "RECROP_RECT:face_recrop_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "TRACKING_RECT:face_tracking_roi"
+  options: {
+    [mediapipe.RoiTrackingCalculatorOptions.ext] {
+      rect_requirements: {
+        rotation_degrees: 15.0
+        translation: 0.1
+        scale: 0.3
+      }
+      landmarks_requirements: {
+        recrop_rect_margin: -0.2
+      }
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_cpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_cpu.pbtxt
new file mode 100644
index 0000000..0a44bcb
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_cpu.pbtxt
@@ -0,0 +1,78 @@
+# Predicts hand landmarks within a ROI derived from hand-related pose landmarks.
+
+type: "HandLandmarksFromPoseCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# Hand-related pose landmarks in [wrist, pinky, index] order.
+# (NormalizedLandmarkList)
+input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
+
+# Hand landmarks. (NormalizedLandmarkList)
+output_stream: "HAND_LANDMARKS:hand_landmarks"
+
+# Debug outputs.
+# Hand ROI derived from hand-related landmarks, which defines the search region
+# for the hand re-crop model. (NormalizedRect)
+output_stream: "HAND_ROI_FROM_POSE:hand_roi_from_pose"
+# Refined hand crop rectangle predicted by hand re-crop model. (NormalizedRect)
+output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
+# Rectangle used to predict hand landmarks. (NormalizedRect)
+output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
+
+# Gets hand visibility.
+node {
+  calculator: "HandVisibilityFromHandLandmarksFromPose"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
+  output_stream: "VISIBILITY:hand_visibility"
+}
+
+# Drops hand-related pose landmarks if pose wrist is not visible. It will
+# prevent from predicting hand landmarks on the current frame.
+node {
+  calculator: "GateCalculator"
+  input_stream: "hand_landmarks_from_pose"
+  input_stream: "ALLOW:hand_visibility"
+  output_stream: "ensured_hand_landmarks_from_pose"
+}
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Gets ROI for re-crop model from hand-related pose landmarks.
+node {
+  calculator: "HandLandmarksFromPoseToRecropRoi"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:ensured_hand_landmarks_from_pose"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:hand_roi_from_pose"
+}
+
+# Predicts hand re-crop rectangle on the current frame.
+node {
+  calculator: "HandRecropByRoiCpu",
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:hand_roi_from_pose"
+  output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
+}
+
+# Gets hand tracking rectangle (either hand rectangle from the previous
+# frame or hand re-crop rectangle from the current frame) for hand prediction.
+node {
+  calculator: "HandTracking"
+  input_stream: "LANDMARKS:hand_landmarks"
+  input_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
+}
+
+# Predicts hand landmarks from the tracking rectangle.
+node {
+  calculator: "HandLandmarkCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:hand_tracking_roi"
+  output_stream: "LANDMARKS:hand_landmarks"
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_gpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_gpu.pbtxt
new file mode 100644
index 0000000..0296e7d
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_gpu.pbtxt
@@ -0,0 +1,78 @@
+# Predicts hand landmarks within a ROI derived from hand-related pose landmarks.
+
+type: "HandLandmarksFromPoseGpu"
+
+# GPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# Hand-related pose landmarks in [wrist, pinky, index] order.
+# (NormalizedLandmarkList)
+input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
+
+# Hand landmarks. (NormalizedLandmarkList)
+output_stream: "HAND_LANDMARKS:hand_landmarks"
+
+# Debug outputs.
+# Hand ROI derived from hand-related landmarks, which defines the search region
+# for the hand re-crop model. (NormalizedRect)
+output_stream: "HAND_ROI_FROM_POSE:hand_roi_from_pose"
+# Refined hand crop rectangle predicted by hand re-crop model. (NormalizedRect)
+output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
+# Rectangle used to predict hand landmarks. (NormalizedRect)
+output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
+
+# Gets hand visibility.
+node {
+  calculator: "HandVisibilityFromHandLandmarksFromPose"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
+  output_stream: "VISIBILITY:hand_visibility"
+}
+
+# Drops hand-related pose landmarks if pose wrist is not visible. It will
+# prevent from predicting hand landmarks on the current frame.
+node {
+  calculator: "GateCalculator"
+  input_stream: "hand_landmarks_from_pose"
+  input_stream: "ALLOW:hand_visibility"
+  output_stream: "ensured_hand_landmarks_from_pose"
+}
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Gets ROI for re-crop model from hand-related pose landmarks.
+node {
+  calculator: "HandLandmarksFromPoseToRecropRoi"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:ensured_hand_landmarks_from_pose"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:hand_roi_from_pose"
+}
+
+# Predicts hand re-crop rectangle on the current frame.
+node {
+  calculator: "HandRecropByRoiGpu",
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:hand_roi_from_pose"
+  output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
+}
+
+# Gets hand tracking rectangle (either hand rectangle from the previous
+# frame or hand re-crop rectangle from the current frame) for hand prediction.
+node {
+  calculator: "HandTracking"
+  input_stream: "LANDMARKS:hand_landmarks"
+  input_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
+}
+
+# Predicts hand landmarks from the tracking rectangle.
+node {
+  calculator: "HandLandmarkGpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "ROI:hand_tracking_roi"
+  output_stream: "LANDMARKS:hand_landmarks"
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_to_recrop_roi.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_to_recrop_roi.pbtxt
new file mode 100644
index 0000000..1c2cfe5
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_to_recrop_roi.pbtxt
@@ -0,0 +1,45 @@
+# Converts hand-related pose landmarks to hand re-crop ROI.
+
+type: "HandLandmarksFromPoseToRecropRoi"
+
+# Hand-related pose landmarks in [wrist, pinky, index] order.
+# (NormalizedLandmarkList)
+input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI to be used for re-crop prediction. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts hand-related pose landmarks to a detection that tightly encloses all
+# of them.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:hand_landmarks_from_pose"
+  output_stream: "DETECTION:hand_detection_from_pose"
+}
+
+# Converts hand detection to a normalized hand rectangle.
+node {
+  calculator: "HandDetectionsFromPoseToRectsCalculator"
+  input_stream: "DETECTION:hand_detection_from_pose"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:hand_roi_from_pose"
+}
+
+# Expands the palm rectangle so that it becomes big enough for hand re-crop
+# model to localize it accurately.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:hand_roi_from_pose"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 2.7
+      scale_y: 2.7
+      shift_y: -0.1
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_cpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_cpu.pbtxt
new file mode 100644
index 0000000..75e0133
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_cpu.pbtxt
@@ -0,0 +1,76 @@
+# Predicts left and right hand landmarks within corresponding ROIs derived from
+# hand-related pose landmarks.
+
+type: "HandLandmarksLeftAndRightCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# Pose landmarks to derive initial hand location from. (NormalizedLandmarkList)
+input_stream: "POSE_LANDMARKS:pose_landmarks"
+
+# Left hand landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+# RIght hand landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+
+# Debug outputs.
+output_stream: "LEFT_HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
+output_stream: "LEFT_HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
+output_stream: "LEFT_HAND_TRACKING_ROI:left_hand_tracking_roi"
+output_stream: "RIGHT_HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
+output_stream: "RIGHT_HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
+output_stream: "RIGHT_HAND_TRACKING_ROI:right_hand_tracking_roi"
+
+# Extracts left-hand-related landmarks from the pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "left_hand_landmarks_from_pose"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 15 end: 16 }
+      ranges: { begin: 17 end: 18 }
+      ranges: { begin: 19 end: 20 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Predicts left hand landmarks.
+node {
+  calculator: "HandLandmarksFromPoseCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:left_hand_landmarks_from_pose"
+  output_stream: "HAND_LANDMARKS:left_hand_landmarks"
+  # Debug outputs.
+  output_stream: "HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
+  output_stream: "HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
+  output_stream: "HAND_TRACKING_ROI:left_hand_tracking_roi"
+}
+
+# Extracts right-hand-related landmarks from the pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "right_hand_landmarks_from_pose"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 16 end: 17 }
+      ranges: { begin: 18 end: 19 }
+      ranges: { begin: 20 end: 21 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Extracts right-hand-related landmarks from the pose landmarks.
+node {
+  calculator: "HandLandmarksFromPoseCpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:right_hand_landmarks_from_pose"
+  output_stream: "HAND_LANDMARKS:right_hand_landmarks"
+  # Debug outputs.
+  output_stream: "HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
+  output_stream: "HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
+  output_stream: "HAND_TRACKING_ROI:right_hand_tracking_roi"
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_gpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_gpu.pbtxt
new file mode 100644
index 0000000..adeec2b
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_gpu.pbtxt
@@ -0,0 +1,76 @@
+# Predicts left and right hand landmarks within corresponding ROIs derived from
+# hand-related pose landmarks.
+
+type: "HandLandmarksLeftAndRightGpu"
+
+# GPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# Pose landmarks to derive initial hand location from. (NormalizedLandmarkList)
+input_stream: "POSE_LANDMARKS:pose_landmarks"
+
+# Left hand landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+# RIght hand landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+
+# Debug outputs.
+output_stream: "LEFT_HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
+output_stream: "LEFT_HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
+output_stream: "LEFT_HAND_TRACKING_ROI:left_hand_tracking_roi"
+output_stream: "RIGHT_HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
+output_stream: "RIGHT_HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
+output_stream: "RIGHT_HAND_TRACKING_ROI:right_hand_tracking_roi"
+
+# Extracts left-hand-related landmarks from the pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "left_hand_landmarks_from_pose"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 15 end: 16 }
+      ranges: { begin: 17 end: 18 }
+      ranges: { begin: 19 end: 20 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Predicts left hand landmarks.
+node {
+  calculator: "HandLandmarksFromPoseGpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:left_hand_landmarks_from_pose"
+  output_stream: "HAND_LANDMARKS:left_hand_landmarks"
+  # Debug outputs.
+  output_stream: "HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
+  output_stream: "HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
+  output_stream: "HAND_TRACKING_ROI:left_hand_tracking_roi"
+}
+
+# Extracts right-hand-related landmarks from the pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "right_hand_landmarks_from_pose"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 16 end: 17 }
+      ranges: { begin: 18 end: 19 }
+      ranges: { begin: 20 end: 21 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Extracts right-hand-related landmarks from the pose landmarks.
+node {
+  calculator: "HandLandmarksFromPoseGpu"
+  input_stream: "IMAGE:input_video"
+  input_stream: "HAND_LANDMARKS_FROM_POSE:right_hand_landmarks_from_pose"
+  output_stream: "HAND_LANDMARKS:right_hand_landmarks"
+  # Debug outputs.
+  output_stream: "HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
+  output_stream: "HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
+  output_stream: "HAND_TRACKING_ROI:right_hand_tracking_roi"
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_to_roi.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_to_roi.pbtxt
new file mode 100644
index 0000000..b874c1d
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_landmarks_to_roi.pbtxt
@@ -0,0 +1,57 @@
+# Converts hand landmarks to ROI.
+
+type: "HandLandmarksToRoi"
+
+# Hand landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:hand_landmarks"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI according to the hand landmarks. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Gets hand palm landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "hand_landmarks"
+  output_stream: "palm_landmarks"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 4 }
+      ranges: { begin: 5 end: 7 }
+      ranges: { begin: 9 end: 11 }
+      ranges: { begin: 13 end: 15 }
+      ranges: { begin: 17 end: 19 }
+      combine_outputs: true
+    }
+  }
+}
+
+# Converts the hand landmarks into a rectangle (normalized by image size)
+# that encloses the hand. The calculator uses a subset of all hand landmarks
+# extracted from SplitNormalizedLandmarkListCalculator above to
+# calculate the bounding box and the rotation of the output rectangle. Please
+# see the comments in the calculator for more detail.
+node {
+  calculator: "HandLandmarksToRectCalculator"
+  input_stream: "NORM_LANDMARKS:palm_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:palm_landmarks_rect"
+}
+
+# Expands the hand rectangle so that it's likely to contain the hand even with
+# some motion.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:palm_landmarks_rect"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 2.0
+      scale_y: 2.0
+      shift_y: -0.1
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_recrop.tflite b/mediapipe/modules/holistic_landmark/hand_recrop.tflite
new file mode 100755
index 0000000..dcfd276
Binary files /dev/null and b/mediapipe/modules/holistic_landmark/hand_recrop.tflite differ
diff --git a/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_cpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_cpu.pbtxt
new file mode 100644
index 0000000..75141d2
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_cpu.pbtxt
@@ -0,0 +1,137 @@
+# Predicts more accurate hand location (re-crop ROI) within a given ROI.
+
+type: "HandRecropByRoiCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# ROI (region of interest) within the given image where a palm/hand is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Refined (more accurate) ROI to use for hand landmark prediction.
+# (NormalizedRect)
+output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop_refined"
+
+# Transforms hand ROI from the input image to a 256x256 tensor. Preserves aspect
+# ratio, which results in a letterbox padding.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:input_video"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:initial_crop_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 256
+      output_tensor_height: 256
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      # For OpenGL origin should be at the top left corner.
+      gpu_origin: TOP_LEFT,
+    }
+  }
+}
+
+# Predicts hand re-crop rectangle.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:initial_crop_tensor"
+  output_stream: "TENSORS:landmark_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model. Two
+# landmarks represent two virtual points: crop and scale of the new crop.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:landmark_tensors"
+  output_stream: "NORM_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 2
+      input_image_width: 256
+      input_image_height: 256
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (hand
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:scaled_landmarks"
+}
+
+# Projects the landmarks from the cropped hand image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:scaled_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:alignment_landmarks"
+}
+
+# Converts hand landmarks to a detection that tightly encloses all landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:alignment_landmarks"
+  output_stream: "DETECTION:hand_detection"
+}
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts hand detection into a rectangle based on center and scale alignment
+# points.
+node {
+  calculator: "AlignmentPointsRectsCalculator"
+  input_stream: "DETECTION:hand_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:hand_roi_from_recrop"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0
+      rotation_vector_end_keypoint_index: 1
+      rotation_vector_target_angle_degrees: -90
+    }
+  }
+}
+
+# TODO: revise hand recrop roi calculation.
+# Slighly moves hand re-crop rectangle from wrist towards fingertips. Due to the
+# new hand cropping logic, crop border is to close to finger tips while a lot of
+# space is below the wrist. And when moving hand up fast (with fingers pointing
+# up) and using hand rect from the previous frame for tracking - fingertips can
+# be cropped. This adjustment partially solves it, but hand cropping logic
+# should be reviewed.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:hand_roi_from_recrop"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "hand_roi_from_recrop_refined"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.0
+      scale_y: 1.0
+      shift_y: -0.1
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_gpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_gpu.pbtxt
new file mode 100644
index 0000000..4fa8f29
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_gpu.pbtxt
@@ -0,0 +1,136 @@
+# Predicts more accurate hand location (re-crop ROI) within a given ROI.
+
+type: "HandRecropByRoiGpu"
+
+# GPU image. (ImageFrame)
+input_stream: "IMAGE:input_video"
+# ROI (region of interest) within the given image where a palm/hand is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Refined (more accurate) ROI to use for hand landmark prediction.
+# (NormalizedRect)
+output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop_refined"
+
+# Transforms hand ROI from the input image to a 256x256 tensor. Preserves aspect
+# ratio, which results in a letterbox padding.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:initial_crop_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 256
+      output_tensor_height: 256
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      # For OpenGL origin should be at the top left corner.
+      gpu_origin: TOP_LEFT,
+    }
+  }
+}
+
+# Predicts hand re-crop rectangle.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:initial_crop_tensor"
+  output_stream: "TENSORS:landmark_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
+    }
+  }
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model. Two
+# landmarks represent two virtual points: crop and scale of the new crop.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:landmark_tensors"
+  output_stream: "NORM_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 2
+      input_image_width: 256
+      input_image_height: 256
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (hand
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:scaled_landmarks"
+}
+
+# Projects the landmarks from the cropped hand image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:scaled_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:alignment_landmarks"
+}
+
+# Converts hand landmarks to a detection that tightly encloses all landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:alignment_landmarks"
+  output_stream: "DETECTION:hand_detection"
+}
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts hand detection into a rectangle based on center and scale alignment
+# points.
+node {
+  calculator: "AlignmentPointsRectsCalculator"
+  input_stream: "DETECTION:hand_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:hand_roi_from_recrop"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0
+      rotation_vector_end_keypoint_index: 1
+      rotation_vector_target_angle_degrees: -90
+    }
+  }
+}
+
+# TODO: revise hand recrop roi calculation.
+# Slighly moves hand re-crop rectangle from wrist towards fingertips. Due to the
+# new hand cropping logic, crop border is to close to finger tips while a lot of
+# space is below the wrist. And when moving hand up fast (with fingers pointing
+# up) and using hand rect from the previous frame for tracking - fingertips can
+# be cropped. This adjustment partially solves it, but hand cropping logic
+# should be reviewed.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:hand_roi_from_recrop"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "hand_roi_from_recrop_refined"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.0
+      scale_y: 1.0
+      shift_y: -0.1
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_tracking.pbtxt b/mediapipe/modules/holistic_landmark/hand_tracking.pbtxt
new file mode 100644
index 0000000..07f734e
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_tracking.pbtxt
@@ -0,0 +1,63 @@
+# Decides what ROI to use for hand landmark prediction: either previous frame
+# landmarks ROI or current frame re-crop ROI.
+
+type: "HandTracking"
+
+# Hand landmarks from the current frame. They will be memorized for tracking on
+# the next frame. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:hand_landmarks"
+# Hand re-crop ROI from the current frame. (NormalizedRect)
+input_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# Hand tracking ROI. Which is either hand landmarks ROI from the previous frame
+# if hand is still tracked, or hand re-crop ROI from the current frame
+# othervise. (NormalizedRect)
+output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
+
+# Keeps track of hand landmarks from the previous frame.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  # TODO: check that loop works with image size instead of video.
+  input_stream: "MAIN:image_size"
+  input_stream: "LOOP:hand_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_hand_landmarks"
+}
+
+# Gets hand landarmsk rect.
+node {
+  calculator: "HandLandmarksToRoi"
+  input_stream: "LANDMARKS:prev_hand_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:prev_hand_landmarks_roi"
+}
+
+# Checks that all requirements for tracking are satisfied and use hand rectangle
+# from the previous frame in that case. Otherwise - use hand re-crop rectangle
+# from the current frame.
+node {
+  calculator: "RoiTrackingCalculator"
+  input_stream: "PREV_LANDMARKS:prev_hand_landmarks"
+  input_stream: "PREV_LANDMARKS_RECT:prev_hand_landmarks_roi"
+  input_stream: "RECROP_RECT:hand_roi_from_recrop"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "TRACKING_RECT:hand_tracking_roi"
+  options: {
+    [mediapipe.RoiTrackingCalculatorOptions.ext] {
+      rect_requirements: {
+        rotation_degrees: 40.0
+        translation: 0.2
+        # TODO: adjust scale for hand tracking.
+        scale: 0.4
+      }
+      landmarks_requirements: {
+        recrop_rect_margin: -0.1
+      }
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_visibility_from_hand_landmarks_from_pose.pbtxt b/mediapipe/modules/holistic_landmark/hand_visibility_from_hand_landmarks_from_pose.pbtxt
new file mode 100644
index 0000000..02db672
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_visibility_from_hand_landmarks_from_pose.pbtxt
@@ -0,0 +1,44 @@
+# Determines hand visibility from the visibility prediction values in the
+# hand-related pose landmarks.
+
+type: "HandVisibilityFromHandLandmarksFromPose"
+
+# Hand-related pose landmarks in [wrist, pinky, index] order.
+# (NormalizedLandmarkList)
+input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
+
+# Hand visibility to be used as a trigger for hand landmark prediction. (bool)
+output_stream: "VISIBILITY:wrist_visibility"
+
+# Gets pose wrist landmark.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "hand_landmarks_from_pose"
+  output_stream: "pose_wrist_landmark"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+    }
+  }
+}
+
+# TODO: Use other than pose wrist palm landmarks.
+# Gets pose wrist visiblity.
+node {
+  calculator: "LandmarkVisibilityCalculator"
+  input_stream: "NORM_LANDMARKS:pose_wrist_landmark"
+  output_stream: "VISIBILITY:wrist_visibility_score"
+}
+
+# TODO: ensure the same threshold in rendering.
+# Converts pose wrist visibility score into boolean flag.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:wrist_visibility_score"
+  output_stream: "FLAG:wrist_visibility"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.1
+    }
+  }
+}
diff --git a/mediapipe/modules/holistic_landmark/hand_wrist_for_pose.pbtxt b/mediapipe/modules/holistic_landmark/hand_wrist_for_pose.pbtxt
new file mode 100644
index 0000000..f6551bb
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/hand_wrist_for_pose.pbtxt
@@ -0,0 +1,52 @@
+# Extracts hand wrist landmark to be used instead of pose wrist landmark.
+
+type: "HandWristForPose"
+
+# Hand landmarks to take wrist landmark from. (NormalizedLandmarkList)
+input_stream: "HAND_LANDMARKS:hand_landmarks"
+
+# Hand wrist landmark to replace original pose wrist landmark with updated
+# visibility. (NormalizedLandmarkList)
+output_stream: "WRIST_LANDMARK:hand_wrist_landmark_with_visibility"
+
+# Side packet with constant for visibility score. As score is `x` from
+# `sigmoid(x)` we pick some big value that doesn't affect pose landmarks
+# visibility rendering threshold.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:visible_score_side_packet"
+  options: {
+    [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+      packet { float_value: 100.0 }
+    }
+  }
+}
+
+# Converts side packet with visibility score to a stream.
+node {
+  calculator: "SidePacketToStreamCalculator"
+  input_stream: "TICK:hand_landmarks"
+  input_side_packet: "visible_score_side_packet"
+  output_stream: "AT_TICK:visible_score"
+}
+
+# Extracts wrist landmark from the hand landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "hand_landmarks"
+  output_stream: "hand_wrist_landmark"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+    }
+  }
+}
+
+# Sets wrist landmark visibility score. If HAND_LANDMARKS is non-empty - wrist
+# will always be visible.
+node {
+  calculator: "SetLandmarkVisibilityCalculator"
+  input_stream: "NORM_LANDMARKS:hand_wrist_landmark"
+  input_stream: "VISIBILITY:visible_score"
+  output_stream: "NORM_LANDMARKS:hand_wrist_landmark_with_visibility"
+}
diff --git a/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt b/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt
new file mode 100644
index 0000000..ce86d1d
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt
@@ -0,0 +1,146 @@
+# Predicts pose + left/right hand + face landmarks.
+#
+# It is required that:
+# - "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+#
+# - "face_landmark.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark.tflite"
+#
+# - "hand_landmark_full.tflite" is available at
+# "mediapipe/modules/hand_landmark/hand_landmark_full.tflite"
+#
+# - "hand_recrop.tflite" is available at
+# "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
+#
+# - "handedness.txt" is available at
+# "mediapipe/modules/hand_landmark/handedness.txt"
+#
+# - "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+#
+# - "pose_landmark_lite.tflite" or "pose_landmark_full.tflite" or
+# "pose_landmark_heavy.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_COMPLEXITY input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "HolisticLandmarkCpu"
+#     input_stream: "IMAGE:input_video"
+#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+#     input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
+#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+#     output_stream: "POSE_LANDMARKS:pose_landmarks"
+#     output_stream: "FACE_LANDMARKS:face_landmarks"
+#     output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+#     output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+#   }
+#
+# NOTE: if a pose/hand/face output is not present in the image, for this
+# particular timestamp there will not be an output packet in the corresponding
+# output stream below. However, the MediaPipe framework will internally inform
+# the downstream calculators of the absence of this packet so that they don't
+# wait for it unnecessarily.
+
+type: "HolisticLandmarkCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether to filter landmarks across different input images to reduce jitter.
+# If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+
+# Whether to predict the segmentation mask. If unspecified, functions as set to
+# false. (bool)
+input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+
+# Whether to filter segmentation mask across different input images to reduce
+# jitter. If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+
+# Whether to run the face landmark model with attention on lips and eyes to
+# provide more accuracy, and additionally output iris landmarks. If unspecified,
+# functions as set to false. (bool)
+input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Pose landmarks. (NormalizedLandmarkList)
+# 33 pose landmarks.
+output_stream: "POSE_LANDMARKS:pose_landmarks"
+# 33 pose world landmarks. (LandmarkList)
+output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
+# 21 left hand landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+# 21 right hand landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+# 468 face landmarks. (NormalizedLandmarkList)
+output_stream: "FACE_LANDMARKS:face_landmarks"
+
+# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Debug outputs
+output_stream: "POSE_ROI:pose_landmarks_roi"
+output_stream: "POSE_DETECTION:pose_detection"
+
+# Predicts pose landmarks.
+node {
+  calculator: "PoseLandmarkCpu"
+  input_stream: "IMAGE:image"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+  output_stream: "LANDMARKS:pose_landmarks"
+  output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+  output_stream: "ROI_FROM_LANDMARKS:pose_landmarks_roi"
+  output_stream: "DETECTION:pose_detection"
+}
+
+# Predicts left and right hand landmarks based on the initial pose landmarks.
+node {
+  calculator: "HandLandmarksLeftAndRightCpu"
+  input_stream: "IMAGE:image"
+  input_stream: "POSE_LANDMARKS:pose_landmarks"
+  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+}
+
+# Extracts face-related pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "face_landmarks_from_pose"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 11 }
+    }
+  }
+}
+
+# Predicts face landmarks based on the initial pose landmarks.
+node {
+  calculator: "FaceLandmarksFromPoseCpu"
+  input_stream: "IMAGE:image"
+  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
+  input_side_packet: "REFINE_LANDMARKS:refine_face_landmarks"
+  output_stream: "FACE_LANDMARKS:face_landmarks"
+}
diff --git a/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt b/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt
new file mode 100644
index 0000000..33ed880
--- /dev/null
+++ b/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt
@@ -0,0 +1,146 @@
+# Predicts pose + left/right hand + face landmarks.
+#
+# It is required that:
+# - "face_detection_short_range.tflite" is available at
+# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
+#
+# - "face_landmark.tflite" is available at
+# "mediapipe/modules/face_landmark/face_landmark.tflite"
+#
+# - "hand_landmark_full.tflite" is available at
+# "mediapipe/modules/hand_landmark/hand_landmark_full.tflite"
+#
+# - "hand_recrop.tflite" is available at
+# "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
+#
+# - "handedness.txt" is available at
+# "mediapipe/modules/hand_landmark/handedness.txt"
+#
+# - "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+#
+# - "pose_landmark_lite.tflite" or "pose_landmark_full.tflite" or
+# "pose_landmark_heavy.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_COMPLEXITY input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "HolisticLandmarkGpu"
+#     input_stream: "IMAGE:input_video"
+#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+#     input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
+#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+#     output_stream: "POSE_LANDMARKS:pose_landmarks"
+#     output_stream: "FACE_LANDMARKS:face_landmarks"
+#     output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+#     output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+#   }
+#
+# NOTE: if a pose/hand/face output is not present in the image, for this
+# particular timestamp there will not be an output packet in the corresponding
+# output stream below. However, the MediaPipe framework will internally inform
+# the downstream calculators of the absence of this packet so that they don't
+# wait for it unnecessarily.
+
+type: "HolisticLandmarkGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether to filter landmarks across different input images to reduce jitter.
+# If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+
+# Whether to predict the segmentation mask. If unspecified, functions as set to
+# false. (bool)
+input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+
+# Whether to filter segmentation mask across different input images to reduce
+# jitter. If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+
+# Whether to run the face landmark model with attention on lips and eyes to
+# provide more accuracy, and additionally output iris landmarks. If unspecified,
+# functions as set to false. (bool)
+input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Pose landmarks. (NormalizedLandmarkList)
+# 33 pose landmarks.
+output_stream: "POSE_LANDMARKS:pose_landmarks"
+# 33 pose world landmarks. (LandmarkList)
+output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
+# 21 left hand landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+# 21 right hand landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+# 468 face landmarks. (NormalizedLandmarkList)
+output_stream: "FACE_LANDMARKS:face_landmarks"
+
+# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Debug outputs
+output_stream: "POSE_ROI:pose_landmarks_roi"
+output_stream: "POSE_DETECTION:pose_detection"
+
+# Predicts pose landmarks.
+node {
+  calculator: "PoseLandmarkGpu"
+  input_stream: "IMAGE:image"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+  output_stream: "LANDMARKS:pose_landmarks"
+  output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+  output_stream: "ROI_FROM_LANDMARKS:pose_landmarks_roi"
+  output_stream: "DETECTION:pose_detection"
+}
+
+# Predicts left and right hand landmarks based on the initial pose landmarks.
+node {
+  calculator: "HandLandmarksLeftAndRightGpu"
+  input_stream: "IMAGE:image"
+  input_stream: "POSE_LANDMARKS:pose_landmarks"
+  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
+  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
+}
+
+# Extracts face-related pose landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "pose_landmarks"
+  output_stream: "face_landmarks_from_pose"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 11 }
+    }
+  }
+}
+
+# Predicts face landmarks based on the initial pose landmarks.
+node {
+  calculator: "FaceLandmarksFromPoseGpu"
+  input_stream: "IMAGE:image"
+  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
+  input_side_packet: "REFINE_LANDMARKS:refine_face_landmarks"
+  output_stream: "FACE_LANDMARKS:face_landmarks"
+}
diff --git a/mediapipe/modules/iris_landmark/BUILD b/mediapipe/modules/iris_landmark/BUILD
new file mode 100644
index 0000000..e16a79b
--- /dev/null
+++ b/mediapipe/modules/iris_landmark/BUILD
@@ -0,0 +1,103 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "iris_landmark_cpu",
+    graph = "iris_landmark_cpu.pbtxt",
+    register_as = "IrisLandmarkCpu",
+    deps = [
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_cropping_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "iris_landmark_gpu",
+    graph = "iris_landmark_gpu.pbtxt",
+    register_as = "IrisLandmarkGpu",
+    deps = [
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_cropping_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
+        "//mediapipe/calculators/tflite:tflite_tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "iris_landmark_left_and_right_gpu",
+    graph = "iris_landmark_left_and_right_gpu.pbtxt",
+    register_as = "IrisLandmarkLeftAndRightGpu",
+    deps = [
+        ":iris_landmark_gpu",
+        ":iris_landmark_landmarks_to_roi",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "iris_landmark_left_and_right_cpu",
+    graph = "iris_landmark_left_and_right_cpu.pbtxt",
+    register_as = "IrisLandmarkLeftAndRightCpu",
+    deps = [
+        ":iris_landmark_cpu",
+        ":iris_landmark_landmarks_to_roi",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "iris_landmark.tflite",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "iris_landmark_landmarks_to_roi",
+    graph = "iris_landmark_landmarks_to_roi.pbtxt",
+    register_as = "IrisLandmarkLandmarksToRoi",
+    deps = [
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
diff --git a/mediapipe/modules/iris_landmark/README.md b/mediapipe/modules/iris_landmark/README.md
new file mode 100644
index 0000000..f99fcee
--- /dev/null
+++ b/mediapipe/modules/iris_landmark/README.md
@@ -0,0 +1,8 @@
+# iris_landmark
+
+Subgraphs|Details
+:--- | :---
+[`IrisLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt)| Detects iris landmarks for left or right eye. (CPU input, and inference is executed on CPU.)
+[`IrisLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt)| Detects iris landmarks for left or right eye. (GPU input, and inference is executed on GPU)
+[`IrisLandmarkLeftAndRightCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt)| Detects iris landmarks for both left and right eyes. (CPU input, and inference is executed on CPU)
+[`IrisLandmarkLeftAndRightGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt)| Detects iris landmarks for both left and right eyes. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/iris_landmark/iris_landmark.tflite b/mediapipe/modules/iris_landmark/iris_landmark.tflite
new file mode 100755
index 0000000..974b910
Binary files /dev/null and b/mediapipe/modules/iris_landmark/iris_landmark.tflite differ
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt
new file mode 100644
index 0000000..f2c4b04
--- /dev/null
+++ b/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt
@@ -0,0 +1,156 @@
+# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
+# a single eye. (CPU input, and inference is executed on CPU.)
+#
+# It is required that "iris_landmark.tflite" is available at
+# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "IrisLandmarkCpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:eye_roi"
+#     input_stream: "IS_RIGHT_EYE:is_right_eye"
+#     output_stream: "EYE_CONTOUR_LANDMARKS:eye_contour_landmarks"
+#     output_stream: "IRIS_LANDMARKS:iris_landmarks"
+#   }
+
+type: "IrisLandmarkCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where an eye is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+# Is right eye. (bool)
+# (Model is trained to detect left eye landmarks only, hence for right eye,
+# flipping is required to immitate left eye.)
+input_stream: "IS_RIGHT_EYE:is_right_eye"
+
+# 71 refined normalized eye contour landmarks. (NormalizedLandmarkList)
+output_stream: "EYE_CONTOUR_LANDMARKS:projected_eye_landmarks"
+# 5 normalized iris landmarks. (NormalizedLandmarkList)
+output_stream: "IRIS_LANDMARKS:projected_iris_landmarks"
+
+node {
+  calculator: "ImageCroppingCalculator"
+  input_stream: "IMAGE:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "IMAGE:eye_image"
+  options: {
+    [mediapipe.ImageCroppingCalculatorOptions.ext] {
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+node {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:eye_image"
+  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
+  output_stream: "IMAGE:transformed_eye_image"
+  output_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 64
+      output_height: 64
+      scale_mode: FIT
+    }
+  }
+}
+
+# Converts the transformed input image on CPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE:transformed_eye_image"
+  output_stream: "TENSORS:image_tensor"
+  options: {
+    [mediapipe.TfLiteConverterCalculatorOptions.ext] {
+      zero_center: false
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/iris_landmark/iris_landmark.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Splits a vector of TFLite tensors to multiple vectors according to the ranges
+# specified in option.
+node {
+  calculator: "SplitTfLiteTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "eye_landmarks_tensor"
+  output_stream: "iris_landmarks_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+    }
+  }
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TfLiteTensorsToLandmarksCalculator"
+  input_stream: "TENSORS:iris_landmarks_tensor"
+  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
+  output_stream: "NORM_LANDMARKS:iris_landmarks"
+  options: {
+    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 5
+      input_image_width: 64
+      input_image_height: 64
+    }
+  }
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TfLiteTensorsToLandmarksCalculator"
+  input_stream: "TENSORS:eye_landmarks_tensor"
+  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
+  output_stream: "NORM_LANDMARKS:eye_landmarks"
+  options: {
+    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 71
+      input_image_width: 64
+      input_image_height: 64
+    }
+  }
+}
+
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:0:iris_landmarks"
+  input_stream: "LANDMARKS:1:eye_landmarks"
+  input_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
+  output_stream: "LANDMARKS:0:padded_iris_landmarks"
+  output_stream: "LANDMARKS:1:padded_eye_landmarks"
+}
+
+# Projects the landmarks from the cropped face image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:0:padded_iris_landmarks"
+  input_stream: "NORM_LANDMARKS:1:padded_eye_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:0:projected_iris_landmarks"
+  output_stream: "NORM_LANDMARKS:1:projected_eye_landmarks"
+}
+
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt
new file mode 100644
index 0000000..9fb7898
--- /dev/null
+++ b/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt
@@ -0,0 +1,162 @@
+# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
+# a single eye. (GPU input, and inference is executed on GPU.)
+#
+# It is required that "iris_landmark.tflite" is available at
+# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "IrisLandmarkGpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:eye_roi"
+#     input_stream: "IS_RIGHT_EYE:is_right_eye"
+#     output_stream: "EYE_CONTOUR_LANDMARKS:eye_contour_landmarks"
+#     output_stream: "IRIS_LANDMARKS:iris_landmarks"
+#   }
+
+type: "IrisLandmarkGpu"
+
+# GPU buffer. (GpuBuffer)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where an eye is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+# Is right eye. (bool)
+# (Model is trained to detect left eye landmarks only, hence for right eye,
+# flipping is required to immitate left eye.)
+input_stream: "IS_RIGHT_EYE:is_right_eye"
+
+# TfLite model to detect iris landmarks.
+# (std::unique_ptr<tflite::FlatBufferModel,
+#      std::function<void(tflite::FlatBufferModel*)>>)
+# NOTE: currently, mediapipe/modules/iris_landmark/iris_landmark.tflite model
+# only, can be passed here, otherwise - results are undefined.
+input_side_packet: "MODEL:model"
+
+# 71 refined normalized eye contour landmarks. (NormalizedLandmarkList)
+output_stream: "EYE_CONTOUR_LANDMARKS:projected_eye_landmarks"
+# 5 normalized iris landmarks. (NormalizedLandmarkList)
+output_stream: "IRIS_LANDMARKS:projected_iris_landmarks"
+
+node {
+  calculator: "ImageCroppingCalculator"
+  input_stream: "IMAGE_GPU:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "IMAGE_GPU:eye_image"
+  options: {
+    [mediapipe.ImageCroppingCalculatorOptions.ext] {
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+node {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:eye_image"
+  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
+  output_stream: "IMAGE_GPU:transformed_eye_image"
+  output_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 64
+      output_height: 64
+      scale_mode: FIT
+    }
+  }
+}
+
+# Converts the transformed input image on CPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE_GPU:transformed_eye_image"
+  output_stream: "TENSORS_GPU:image_tensor"
+  options: {
+    [mediapipe.TfLiteConverterCalculatorOptions.ext] {
+      zero_center: false
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS_GPU:image_tensor"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/iris_landmark/iris_landmark.tflite"
+    }
+  }
+}
+
+# Splits a vector of TFLite tensors to multiple vectors according to the ranges
+# specified in option.
+node {
+  calculator: "SplitTfLiteTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "eye_landmarks_tensor"
+  output_stream: "iris_landmarks_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+    }
+  }
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TfLiteTensorsToLandmarksCalculator"
+  input_stream: "TENSORS:iris_landmarks_tensor"
+  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
+  output_stream: "NORM_LANDMARKS:iris_landmarks"
+  options: {
+    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 5
+      input_image_width: 64
+      input_image_height: 64
+    }
+  }
+}
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TfLiteTensorsToLandmarksCalculator"
+  input_stream: "TENSORS:eye_landmarks_tensor"
+  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
+  output_stream: "NORM_LANDMARKS:eye_landmarks"
+  options: {
+    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 71
+      input_image_width: 64
+      input_image_height: 64
+    }
+  }
+}
+
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:0:iris_landmarks"
+  input_stream: "LANDMARKS:1:eye_landmarks"
+  input_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
+  output_stream: "LANDMARKS:0:padded_iris_landmarks"
+  output_stream: "LANDMARKS:1:padded_eye_landmarks"
+}
+
+# Projects the landmarks from the cropped face image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:0:padded_iris_landmarks"
+  input_stream: "NORM_LANDMARKS:1:padded_eye_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:0:projected_iris_landmarks"
+  output_stream: "NORM_LANDMARKS:1:projected_eye_landmarks"
+}
+
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_landmarks_to_roi.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_landmarks_to_roi.pbtxt
new file mode 100644
index 0000000..fc53a16
--- /dev/null
+++ b/mediapipe/modules/iris_landmark/iris_landmark_landmarks_to_roi.pbtxt
@@ -0,0 +1,50 @@
+# MediaPipe subgraph to calculate region of interest (ROI) which is then can
+# be used to calculate iris landmarks and eye contour landmarks.
+#
+# NOTE: this graph is subject to change and should not be used directly.
+
+type: "IrisLandmarkLandmarksToRoi"
+
+# List of two normalized landmarks: left and right corners of an eye.
+# (NormalizedLandmarkList)
+input_stream: "LANDMARKS:landmarks"
+# Image size. (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI (region of interest) within the given image where an eye is located.
+# (NormalizedRect)
+output_stream: "ROI:roi"
+
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  output_stream: "DETECTION:detection"
+}
+
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTION:detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:raw_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0
+      rotation_vector_end_keypoint_index: 1
+      rotation_vector_target_angle_degrees: 0
+    }
+  }
+}
+
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:raw_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 2.3
+      scale_y: 2.3
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt
new file mode 100644
index 0000000..7fb72de
--- /dev/null
+++ b/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt
@@ -0,0 +1,120 @@
+# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
+# two eyes: left and right. (CPU input, and inference is executed on CPU.)
+#
+# It is required that "iris_landmark.tflite" is available at
+# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "IrisLandmarkLeftAndRightCpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+#     input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+#     output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+#     output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+#     output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+#     output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+#   }
+
+type: "IrisLandmarkLeftAndRightCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+# List of two landmarks defining LEFT eye boundaries - left and right corners.
+# (NormalizedLandmarkList)
+input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+# List of two landmarks defining RIGHT eye boundaries - left and right corners.
+# (NormalizedLandmarkList)
+input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+
+# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+# 5 normalized iris landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+# Region of interest used to do calculations for the left eye. (NormalizedRect)
+output_stream: "LEFT_EYE_ROI:left_eye_roi"
+
+# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+# 5 normalized iris landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+# Region of interest used to do calculations for the right eye. (NormalizedRect)
+output_stream: "RIGHT_EYE_ROI:right_eye_roi"
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:image"
+  output_stream: "SIZE:image_size"
+}
+
+### Processing left eye ###
+
+node {
+  calculator: "IrisLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:left_eye_boundary_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:left_eye_roi"
+}
+
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:left_eye_flag_side_packet"
+  options {
+    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
+      packet { bool_value: false }
+    }
+  }
+}
+
+node {
+  calculator: "SidePacketToStreamCalculator"
+  input_stream: "TICK:image"
+  input_side_packet: "left_eye_flag_side_packet"
+  output_stream: "AT_TICK:left_eye_flag"
+}
+
+node {
+  calculator: "IrisLandmarkCpu"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:left_eye_roi"
+  input_stream: "IS_RIGHT_EYE:left_eye_flag"
+  output_stream: "EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "IRIS_LANDMARKS:left_iris_landmarks"
+}
+
+### Processing right eye ###
+
+node {
+  calculator: "IrisLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:right_eye_boundary_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:right_eye_roi"
+}
+
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:right_eye_flag_side_packet"
+  options {
+    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
+      packet { bool_value: true }
+    }
+  }
+}
+
+node {
+  calculator: "SidePacketToStreamCalculator"
+  input_stream: "TICK:image"
+  input_side_packet: "right_eye_flag_side_packet"
+  output_stream: "AT_TICK:right_eye_flag"
+}
+
+node {
+  calculator: "IrisLandmarkCpu"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:right_eye_roi"
+  input_stream: "IS_RIGHT_EYE:right_eye_flag"
+  output_stream: "EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "IRIS_LANDMARKS:right_iris_landmarks"
+}
+
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt
new file mode 100644
index 0000000..eeff026
--- /dev/null
+++ b/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt
@@ -0,0 +1,120 @@
+# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
+# two eyes: left and right. (GPU input, and inference is executed on GPU.)
+#
+# It is required that "iris_landmark.tflite" is available at
+# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "IrisLandmarkLeftAndRightGpu"
+#     input_stream: "IMAGE:image"
+#     input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+#     input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+#     output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+#     output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+#     output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+#     output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+#   }
+
+type: "IrisLandmarkLeftAndRightGpu"
+
+# GPU buffer. (GpuBuffer)
+input_stream: "IMAGE:image"
+# List of two landmarks defining LEFT eye boundaries - left and right corners.
+# (NormalizedLandmarkList)
+input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
+# List of two landmarks defining RIGHT eye boundaries - left and right corners.
+# (NormalizedLandmarkList)
+input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
+
+# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+# 5 normalized iris landmarks. (NormalizedLandmarkList)
+output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
+# Region of interest used to do calculations for the left eye. (NormalizedRect)
+output_stream: "LEFT_EYE_ROI:left_eye_roi"
+
+# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+# 5 normalized iris landmarks. (NormalizedLandmarkList)
+output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
+# Region of interest used to do calculations for the right eye. (NormalizedRect)
+output_stream: "RIGHT_EYE_ROI:right_eye_roi"
+
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+### Processing left eye ###
+
+node {
+  calculator: "IrisLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:left_eye_boundary_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:left_eye_roi"
+}
+
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:left_eye_flag_side_packet"
+  options {
+    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
+      packet { bool_value: false }
+    }
+  }
+}
+
+node {
+  calculator: "SidePacketToStreamCalculator"
+  input_stream: "TICK:image"
+  input_side_packet: "left_eye_flag_side_packet"
+  output_stream: "AT_TICK:left_eye_flag"
+}
+
+node {
+  calculator: "IrisLandmarkGpu"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:left_eye_roi"
+  input_stream: "IS_RIGHT_EYE:left_eye_flag"
+  output_stream: "EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
+  output_stream: "IRIS_LANDMARKS:left_iris_landmarks"
+}
+
+### Processing right eye ###
+
+node {
+  calculator: "IrisLandmarkLandmarksToRoi"
+  input_stream: "LANDMARKS:right_eye_boundary_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:right_eye_roi"
+}
+
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:right_eye_flag_side_packet"
+  options {
+    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
+      packet { bool_value: true }
+    }
+  }
+}
+
+node {
+  calculator: "SidePacketToStreamCalculator"
+  input_stream: "TICK:image"
+  input_side_packet: "right_eye_flag_side_packet"
+  output_stream: "AT_TICK:right_eye_flag"
+}
+
+node {
+  calculator: "IrisLandmarkGpu"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:right_eye_roi"
+  input_stream: "IS_RIGHT_EYE:right_eye_flag"
+  output_stream: "EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
+  output_stream: "IRIS_LANDMARKS:right_iris_landmarks"
+}
+
diff --git a/mediapipe/modules/objectron/BUILD b/mediapipe/modules/objectron/BUILD
new file mode 100644
index 0000000..cee5768
--- /dev/null
+++ b/mediapipe/modules/objectron/BUILD
@@ -0,0 +1,183 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+exports_files([
+    "object_detection_3d_camera.tflite",
+    "object_detection_3d_chair.tflite",
+    "object_detection_3d_chair_1stage.tflite",
+    "object_detection_3d_cup.tflite",
+    "object_detection_3d_sneakers.tflite",
+    "object_detection_3d_sneakers_1stage.tflite",
+    "object_detection_oidv4_labelmap.txt",
+    "object_detection_ssd_mobilenetv2_oidv4_fp16.tflite",
+])
+
+mediapipe_simple_subgraph(
+    name = "objectron_detection_1stage_gpu",
+    graph = "objectron_detection_1stage_gpu.pbtxt",
+    register_as = "ObjectronDetection1StageSubgraphGpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tflite:tflite_converter_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/tflite:tflite_inference_calculator",
+        "//mediapipe/modules/objectron/calculators:tflite_tensors_to_objects_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "objectron_tracking_1stage_gpu",
+    graph = "objectron_tracking_1stage_gpu.pbtxt",
+    register_as = "ObjectronTracking1StageSubgraphGpu",
+    deps = [
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/video:box_tracker_calculator",
+        "//mediapipe/calculators/video:flow_packager_calculator",
+        "//mediapipe/calculators/video:motion_analysis_calculator",
+        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
+        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
+        "//mediapipe/modules/objectron/calculators:frame_annotation_to_timed_box_list_calculator",
+        "//mediapipe/modules/objectron/calculators:frame_annotation_tracker_calculator",
+        "//mediapipe/modules/objectron/calculators:lift_2d_frame_annotation_to_3d_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "box_landmark_gpu",
+    graph = "box_landmark_gpu.pbtxt",
+    register_as = "BoxLandmarkSubgraph",
+    deps = [
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "box_landmark_cpu",
+    graph = "box_landmark_cpu.pbtxt",
+    register_as = "BoxLandmarkSubgraph",
+    deps = [
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "object_detection_oid_v4_gpu",
+    graph = "object_detection_oid_v4_gpu.pbtxt",
+    register_as = "ObjectDetectionOidV4Subgraph",
+    deps = [
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+        "//mediapipe/modules/objectron/calculators:filter_detection_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "object_detection_oid_v4_cpu",
+    graph = "object_detection_oid_v4_cpu.pbtxt",
+    register_as = "ObjectDetectionOidV4Subgraph",
+    deps = [
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+        "//mediapipe/modules/objectron/calculators:filter_detection_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "objectron_cpu",
+    graph = "objectron_cpu.pbtxt",
+    register_as = "ObjectronCpuSubgraph",
+    deps = [
+        ":box_landmark_cpu",
+        ":object_detection_oid_v4_cpu",
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tflite:tflite_model_calculator",
+        "//mediapipe/calculators/util:association_norm_rect_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/calculators/util:local_file_contents_calculator",
+        "//mediapipe/modules/objectron/calculators:frame_annotation_to_rect_calculator",
+        "//mediapipe/modules/objectron/calculators:landmarks_to_frame_annotation_calculator",
+        "//mediapipe/modules/objectron/calculators:lift_2d_frame_annotation_to_3d_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "objectron_gpu",
+    graph = "objectron_gpu.pbtxt",
+    register_as = "ObjectronGpuSubgraph",
+    deps = [
+        ":box_landmark_gpu",
+        ":object_detection_oid_v4_gpu",
+        "//mediapipe/calculators/core:begin_loop_calculator",
+        "//mediapipe/calculators/core:clip_vector_size_calculator",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:end_loop_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:association_norm_rect_calculator",
+        "//mediapipe/calculators/util:collection_has_min_size_calculator",
+        "//mediapipe/calculators/util:detections_to_rects_calculator",
+        "//mediapipe/modules/objectron/calculators:frame_annotation_to_rect_calculator",
+        "//mediapipe/modules/objectron/calculators:landmarks_to_frame_annotation_calculator",
+        "//mediapipe/modules/objectron/calculators:lift_2d_frame_annotation_to_3d_calculator",
+    ],
+)
diff --git a/mediapipe/modules/objectron/README.md b/mediapipe/modules/objectron/README.md
new file mode 100644
index 0000000..00883fe
--- /dev/null
+++ b/mediapipe/modules/objectron/README.md
@@ -0,0 +1,6 @@
+# objectron
+
+Subgraphs|Details
+:--- | :---
+[`ObjectronCpuSubgraph`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_cpu.pbtxt)| Detects and tracks 3D bounding boxes for objects. (CPU input, and inference is executed on CPU.)
+[`ObjectronGpuSubgraph`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_gpu.pbtxt)| Detects and tracks 3D bounding boxes for objects. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/objectron/box_landmark_cpu.pbtxt b/mediapipe/modules/objectron/box_landmark_cpu.pbtxt
new file mode 100644
index 0000000..bb638d1
--- /dev/null
+++ b/mediapipe/modules/objectron/box_landmark_cpu.pbtxt
@@ -0,0 +1,147 @@
+# MediaPipe Box landmark localization CPU subgraph.
+
+type: "BoxLandmarkSubgraph"
+
+input_stream: "IMAGE:image"
+input_stream: "NORM_RECT:box_rect"
+input_side_packet: "MODEL:model"
+output_stream: "NORM_LANDMARKS:box_landmarks"
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Expands the rectangle that contain the box so that it's likely to cover the
+# entire box.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:box_rect"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "box_rect_scaled"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.5
+      scale_y: 1.5
+      square_long: true
+    }
+  }
+}
+
+# Crops, resizes, and converts the input video into tensor.
+# Preserves aspect ratio of the images.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:image"
+  input_stream: "NORM_RECT:box_rect_scaled"
+  output_stream: "TENSORS:image_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 224
+      output_tensor_height: 224
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      gpu_origin: TOP_LEFT
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  input_side_packet: "MODEL:model"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Splits a vector of tensors into multiple vectors.
+node {
+  calculator: "SplitTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "box_flag_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+    }
+  }
+}
+
+# Converts the box-flag tensor into a float that represents the confidence
+# score of box presence.
+node {
+  calculator: "TensorsToFloatsCalculator"
+  input_stream: "TENSORS:box_flag_tensor"
+  output_stream: "FLOAT:box_presence_score"
+}
+
+# Applies a threshold to the confidence score to determine whether a box is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:box_presence_score"
+  output_stream: "FLAG:box_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.99
+    }
+  }
+}
+
+# Drops landmarks tensors if box is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:box_presence"
+  output_stream: "gated_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a list of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:gated_landmark_tensors"
+  output_stream: "NORM_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 9
+      input_image_width: 224
+      input_image_height: 224
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed box
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (box
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:scaled_landmarks"
+}
+
+# Projects the landmarks from the cropped box image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:scaled_landmarks"
+  input_stream: "NORM_RECT:box_rect_scaled"
+  output_stream: "NORM_LANDMARKS:box_landmarks"
+}
diff --git a/mediapipe/modules/objectron/box_landmark_gpu.pbtxt b/mediapipe/modules/objectron/box_landmark_gpu.pbtxt
new file mode 100644
index 0000000..ac95880
--- /dev/null
+++ b/mediapipe/modules/objectron/box_landmark_gpu.pbtxt
@@ -0,0 +1,147 @@
+# MediaPipe Box landmark localization GPU subgraph.
+
+type: "BoxLandmarkSubgraph"
+
+input_stream: "IMAGE:image"
+input_stream: "NORM_RECT:box_rect"
+output_stream: "NORM_LANDMARKS:box_landmarks"
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Expands the rectangle that contain the box so that it's likely to cover the
+# entire box.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:box_rect"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "box_rect_scaled"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.5
+      scale_y: 1.5
+      square_long: true
+    }
+  }
+}
+
+# Crops, resizes, and converts the input video into tensor.
+# Preserves aspect ratio of the images.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:image"
+  input_stream: "NORM_RECT:box_rect_scaled"
+  output_stream: "TENSORS:image_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 224
+      output_tensor_height: 224
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      gpu_origin: TOP_LEFT
+      border_mode: BORDER_REPLICATE
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "object_detection_3d.tflite"
+      delegate { gpu {} }
+    }
+  }
+}
+
+# Splits a vector of tensors to multiple vectors according to the ranges
+# specified in option.
+node {
+  calculator: "SplitTensorVectorCalculator"
+  input_stream: "output_tensors"
+  output_stream: "landmark_tensors"
+  output_stream: "box_flag_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+    }
+  }
+}
+
+# Converts the box-flag tensor into a float that represents the confidence
+# score of box presence.
+node {
+  calculator: "TensorsToFloatsCalculator"
+  input_stream: "TENSORS:box_flag_tensor"
+  output_stream: "FLOAT:box_presence_score"
+}
+
+# Applies a threshold to the confidence score to determine whether a box is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:box_presence_score"
+  output_stream: "FLAG:box_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.99
+    }
+  }
+}
+
+# Drops landmarks tensors if box is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensors"
+  input_stream: "ALLOW:box_presence"
+  output_stream: "gated_landmark_tensors"
+}
+
+# Decodes the landmark tensors into a list of landmarks, where the landmark
+# coordinates are normalized by the size of the input image to the model.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:gated_landmark_tensors"
+  output_stream: "NORM_LANDMARKS:landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 9
+      input_image_width: 224
+      input_image_height: 224
+    }
+  }
+}
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed box
+# image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (box
+# image before image transformation).
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:scaled_landmarks"
+}
+
+# Projects the landmarks from the cropped box image to the corresponding
+# locations on the full image before cropping (input to the graph).
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:scaled_landmarks"
+  input_stream: "NORM_RECT:box_rect_scaled"
+  output_stream: "NORM_LANDMARKS:box_landmarks"
+}
diff --git a/mediapipe/modules/objectron/calculators/BUILD b/mediapipe/modules/objectron/calculators/BUILD
new file mode 100644
index 0000000..fb75eb3
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/BUILD
@@ -0,0 +1,407 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
+load("//mediapipe/framework:mediapipe_register_type.bzl", "mediapipe_register_type")
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_proto_library(
+    name = "object_proto",
+    srcs = ["object.proto"],
+    visibility = ["//visibility:public"],
+)
+
+mediapipe_proto_library(
+    name = "a_r_capture_metadata_proto",
+    srcs = ["a_r_capture_metadata.proto"],
+    visibility = ["//visibility:public"],
+)
+
+mediapipe_proto_library(
+    name = "annotation_proto",
+    srcs = ["annotation_data.proto"],
+    def_options_lib = False,
+    visibility = ["//visibility:public"],
+    deps = [
+        ":a_r_capture_metadata_proto",
+        ":object_proto",
+    ],
+)
+
+mediapipe_register_type(
+    base_name = "annotation",
+    include_headers = ["mediapipe/modules/objectron/calculators/annotation_data.pb.h"],
+    types = [
+        "::mediapipe::FrameAnnotation",
+    ],
+    deps = [":annotation_cc_proto"],
+)
+
+mediapipe_proto_library(
+    name = "camera_parameters_proto",
+    srcs = ["camera_parameters.proto"],
+    visibility = ["//visibility:public"],
+)
+
+mediapipe_proto_library(
+    name = "frame_annotation_tracker_calculator_proto",
+    srcs = ["frame_annotation_tracker_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "belief_decoder_config_proto",
+    srcs = ["belief_decoder_config.proto"],
+    visibility = ["//visibility:public"],
+)
+
+mediapipe_proto_library(
+    name = "tflite_tensors_to_objects_calculator_proto",
+    srcs = ["tflite_tensors_to_objects_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":belief_decoder_config_proto",
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "tensors_to_objects_calculator_proto",
+    srcs = ["tensors_to_objects_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":belief_decoder_config_proto",
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "lift_2d_frame_annotation_to_3d_calculator_proto",
+    srcs = ["lift_2d_frame_annotation_to_3d_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":belief_decoder_config_proto",
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "frame_annotation_to_rect_calculator_proto",
+    srcs = ["frame_annotation_to_rect_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "filter_detection_calculator_proto",
+    srcs = ["filter_detection_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+cc_library(
+    name = "box_util",
+    srcs = ["box_util.cc"],
+    hdrs = ["box_util.h"],
+    deps = [
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/util/tracking:box_tracker_cc_proto",
+    ],
+)
+
+cc_library(
+    name = "frame_annotation_tracker",
+    srcs = ["frame_annotation_tracker.cc"],
+    hdrs = ["frame_annotation_tracker.h"],
+    deps = [
+        ":annotation_cc_proto",
+        ":box_util",
+        "//mediapipe/framework/port:integral_types",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/util/tracking:box_tracker_cc_proto",
+        "@com_google_absl//absl/container:btree",
+        "@com_google_absl//absl/container:flat_hash_set",
+    ],
+)
+
+cc_library(
+    name = "epnp",
+    srcs = [
+        "epnp.cc",
+    ],
+    hdrs = [
+        "epnp.h",
+    ],
+    deps = [
+        "//mediapipe/framework/port:logging",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/strings:str_format",
+        "@eigen_archive//:eigen3",
+    ],
+)
+
+cc_library(
+    name = "decoder",
+    srcs = [
+        "decoder.cc",
+    ],
+    hdrs = [
+        "decoder.h",
+    ],
+    deps = [
+        ":annotation_cc_proto",
+        ":belief_decoder_config_cc_proto",
+        ":box",
+        ":epnp",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:status",
+        "@com_google_absl//absl/status",
+        "@eigen_archive//:eigen3",
+    ],
+)
+
+cc_library(
+    name = "tensor_util",
+    srcs = [
+        "tensor_util.cc",
+    ],
+    hdrs = [
+        "tensor_util.h",
+    ],
+    deps = [
+        "//mediapipe/framework/formats:tensor",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:opencv_core",
+        "@org_tensorflow//tensorflow/lite:framework",
+    ],
+)
+
+cc_library(
+    name = "box",
+    srcs = [
+        "box.cc",
+        "model.cc",
+    ],
+    hdrs = [
+        "box.h",
+        "model.h",
+        "types.h",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotation_cc_proto",
+        ":object_cc_proto",
+        "//mediapipe/framework/port:logging",
+        "@eigen_archive//:eigen3",
+    ],
+)
+
+cc_library(
+    name = "frame_annotation_to_timed_box_list_calculator",
+    srcs = ["frame_annotation_to_timed_box_list_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotation_cc_proto",
+        ":box_util",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/util/tracking:box_tracker_cc_proto",
+        "@com_google_absl//absl/memory",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "frame_annotation_tracker_calculator",
+    srcs = ["frame_annotation_tracker_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotation_cc_proto",
+        ":frame_annotation_tracker",
+        ":frame_annotation_tracker_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/util/tracking:box_tracker_cc_proto",
+        "@com_google_absl//absl/container:flat_hash_set",
+        "@com_google_absl//absl/memory",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "tflite_tensors_to_objects_calculator",
+    srcs = ["tflite_tensors_to_objects_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotation_cc_proto",
+        ":belief_decoder_config_cc_proto",
+        ":decoder",
+        ":tensor_util",
+        ":tflite_tensors_to_objects_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/formats:detection_cc_proto",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:ret_check",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings:str_format",
+        "@com_google_absl//absl/types:span",
+        "@eigen_archive//:eigen3",
+        "@org_tensorflow//tensorflow/lite:framework",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "tensors_to_objects_calculator",
+    srcs = ["tensors_to_objects_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotation_cc_proto",
+        ":belief_decoder_config_cc_proto",
+        ":decoder",
+        ":tensor_util",
+        ":tensors_to_objects_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/formats:detection_cc_proto",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:ret_check",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings:str_format",
+        "@com_google_absl//absl/types:span",
+        "@eigen_archive//:eigen3",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "lift_2d_frame_annotation_to_3d_calculator",
+    srcs = ["lift_2d_frame_annotation_to_3d_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":annotation_cc_proto",
+        ":belief_decoder_config_cc_proto",
+        ":decoder",
+        ":lift_2d_frame_annotation_to_3d_calculator_cc_proto",
+        ":tensor_util",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/formats:detection_cc_proto",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:ret_check",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings:str_format",
+        "@com_google_absl//absl/types:span",
+        "@eigen_archive//:eigen3",
+        "@org_tensorflow//tensorflow/lite:framework",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "frame_annotation_to_rect_calculator",
+    srcs = ["frame_annotation_to_rect_calculator.cc"],
+    deps = [
+        ":annotation_cc_proto",
+        ":frame_annotation_to_rect_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "@com_google_absl//absl/memory",
+        "@eigen_archive//:eigen3",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "landmarks_to_frame_annotation_calculator",
+    srcs = ["landmarks_to_frame_annotation_calculator.cc"],
+    deps = [
+        ":annotation_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+        "@com_google_absl//absl/memory",
+    ],
+    alwayslink = 1,
+)
+
+cc_library(
+    name = "filter_detection_calculator",
+    srcs = ["filter_detection_calculator.cc"],
+    deps = [
+        ":filter_detection_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:detection_cc_proto",
+        "//mediapipe/framework/formats:location_data_cc_proto",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:map_util",
+        "//mediapipe/framework/port:re2",
+        "//mediapipe/framework/port:status",
+        "@com_google_absl//absl/container:node_hash_set",
+        "@com_google_absl//absl/strings",
+    ],
+    alwayslink = 1,
+)
+
+cc_test(
+    name = "box_util_test",
+    srcs = ["box_util_test.cc"],
+    deps = [
+        ":box_util",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/util/tracking:box_tracker_cc_proto",
+    ],
+)
+
+cc_test(
+    name = "frame_annotation_tracker_test",
+    srcs = ["frame_annotation_tracker_test.cc"],
+    deps = [
+        ":annotation_cc_proto",
+        ":frame_annotation_tracker",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/util/tracking:box_tracker_cc_proto",
+        "@com_google_absl//absl/container:flat_hash_set",
+    ],
+)
diff --git a/mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto b/mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto
new file mode 100644
index 0000000..edc8c4b
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto
@@ -0,0 +1,551 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+// Info about the camera characteristics used to capture images and depth data.
+// See developer.apple.com/documentation/avfoundation/avcameracalibrationdata
+// for more information.
+message AVCameraCalibrationData {
+  // 3x3 row-major matrix relating a camera's internal properties to an ideal
+  // pinhole-camera model.
+  // See
+  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881135-intrinsicmatrix
+  // for detailed usage information.
+  repeated float intrinsic_matrix = 1 [packed = true];
+
+  // The image dimensions to which the intrinsic_matrix values are relative.
+  optional float intrinsic_matrix_reference_dimension_width = 2;
+  optional float intrinsic_matrix_reference_dimension_height = 3;
+
+  // 3x4 row-major matrix relating a camera's position and orientation to a
+  // world or scene coordinate system. Consists of a unitless 3x3 rotation
+  // matrix (R) on the left and a translation (t) 3x1 vector on the right. The
+  // translation vector's units are millimeters. For example:
+  //
+  //            |r1,1  r2,1  r3,1 | t1|
+  //  [R | t] = |r1,2  r2,2  r3,2 | t2|
+  //            |r1,3  r2,3  r3,3 | t3|
+  //
+  //  is stored as [r11, r21, r31, t1, r12, r22, r32, t2, ...]
+  //
+  // See
+  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881130-extrinsicmatrix?language=objc
+  // for more information.
+  repeated float extrinsic_matrix = 4 [packed = true];
+
+  // The size, in millimeters, of one image pixel.
+  optional float pixel_size = 5;
+
+  // A list of floating-point values describing radial distortions imparted by
+  // the camera lens, for use in rectifying camera images.
+  // See
+  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881129-lensdistortionlookuptable?language=objc
+  // for more information.
+  repeated float lens_distortion_lookup_values = 6 [packed = true];
+
+  // A list of floating-point values describing radial distortions for use in
+  // reapplying camera geometry to a rectified image.
+  // See
+  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881132-inverselensdistortionlookuptable?language=objc
+  // for more information.
+  repeated float inverse_lens_distortion_lookup_values = 7 [packed = true];
+
+  // The offset of the distortion center of the camera lens from the top-left
+  // corner of the image.
+  // See
+  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881131-lensdistortioncenter?language=objc
+  // for more information.
+  optional float lens_distortion_center_x = 8;
+  optional float lens_distortion_center_y = 9;
+}
+
+// Container for depth data information.
+// See developer.apple.com/documentation/avfoundation/avdepthdata for more info.
+message AVDepthData {
+  // PNG representation of the grayscale depth data map. See discussion about
+  // depth_data_map_original_minimum_value, below, for information about how
+  // to interpret the pixel values.
+  optional bytes depth_data_map = 1;
+
+  // Pixel format type of the original captured depth data.
+  // See
+  // developer.apple.com/documentation/corevideo/1563591-pixel_format_identifiers?language=objc
+  // for the complete list of possible pixel format types. This value represents
+  // a string for the associated OSType/FourCharCode.
+  optional string depth_data_type = 2;
+
+  // Indicates the general accuracy of the depth_data_map.
+  // See developer.apple.com/documentation/avfoundation/avdepthdataaccuracy for
+  // more information.
+  enum Accuracy {
+    UNDEFINED_ACCURACY = 0;
+    // Values in the depth map are usable for foreground/background separation
+    // but are not absolutely accurate in the physical world.
+    RELATIVE = 1;
+    // Values in the depth map are absolutely accurate in the physical world.
+    ABSOLUTE = 2;
+  }
+  optional Accuracy depth_data_accuracy = 3 [default = RELATIVE];
+
+  // Indicates whether the depth_data_map contains temporally smoothed data.
+  optional bool depth_data_filtered = 4;
+
+  // Quality of the depth_data_map.
+  enum Quality {
+    UNDEFINED_QUALITY = 0;
+    HIGH = 1;
+    LOW = 2;
+  }
+  optional Quality depth_data_quality = 5;
+
+  // Associated calibration data for the depth_data_map.
+  optional AVCameraCalibrationData camera_calibration_data = 6;
+
+  // The original range of values expressed by the depth_data_map, before
+  // grayscale normalization. For example, if the minimum and maximum values
+  // indicate a range of [0.5, 2.2], and the depth_data_type value indicates
+  // it was a depth map, then white pixels (255, 255, 255) will map to 0.5 and
+  // black pixels (0, 0, 0) will map to 2.2 with the grayscale range linearly
+  // interpolated inbetween. Conversely, if the depth_data_type value indicates
+  // it was a disparity map, then white pixels will map to 2.2 and black pixels
+  // will map to 0.5.
+  optional float depth_data_map_original_minimum_value = 7;
+  optional float depth_data_map_original_maximum_value = 8;
+
+  // The width of the depth buffer map.
+  optional int32 depth_data_map_width = 9;
+
+  // The height of the depth buffer map.
+  optional int32 depth_data_map_height = 10;
+
+  // The row-major flattened array of the depth buffer map pixels. This will be
+  // either a float32 or float16 byte array, depending on 'depth_data_type'.
+  optional bytes depth_data_map_raw_values = 11;
+}
+
+// Estimated scene lighting information associated with a captured video frame.
+// See developer.apple.com/documentation/arkit/arlightestimate for more info.
+message ARLightEstimate {
+  // The estimated intensity, in lumens, of ambient light throughout the scene.
+  optional double ambient_intensity = 1;
+
+  // The estimated color temperature, in degrees Kelvin, of ambient light
+  // throughout the scene.
+  optional double ambient_color_temperature = 2;
+
+  // Data describing the estimated lighting environment in all directions.
+  // Second-level spherical harmonics in separate red, green, and blue data
+  // planes. Thus, this buffer contains 3 sets of 9 coefficients, or a total of
+  // 27 values.
+  // See
+  // https://developer.apple.com/documentation/arkit/ardirectionallightestimate/2928222-sphericalharmonicscoefficients?language=objc
+  // for more information.
+  repeated float spherical_harmonics_coefficients = 3 [packed = true];
+
+  message DirectionVector {
+    optional float x = 1;
+    optional float y = 2;
+    optional float z = 3;
+  }
+  // A vector indicating the orientation of the strongest directional light
+  // source, normalized in the world-coordinate space.
+  // See
+  // https://developer.apple.com/documentation/arkit/ardirectionallightestimate/2928221-primarylightdirection?language=objc
+  // for more information;
+  optional DirectionVector primary_light_direction = 4;
+
+  // The estimated intensity, in lumens, of the strongest directional light
+  // source in the scene.
+  // See
+  // https://developer.apple.com/documentation/arkit/ardirectionallightestimate/2928219-primarylightintensity?language=objc
+  // for more information.
+  optional float primary_light_intensity = 5;
+}
+
+// Information about the camera position and imaging characteristics for a
+// captured video frame.
+// See developer.apple.com/documentation/arkit/arcamera for more information.
+message ARCamera {
+  // The general quality of position tracking available when the camera captured
+  // a frame.
+  enum TrackingState {
+    UNDEFINED_TRACKING_STATE = 0;
+    // Camera position tracking is not available.
+    UNAVAILABLE = 1;
+    // Tracking is available, but the quality of results is questionable.
+    LIMITED = 2;
+    // Camera position tracking is providing optimal results.
+    NORMAL = 3;
+  }
+  optional TrackingState tracking_state = 1 [default = UNAVAILABLE];
+
+  // A possible diagnosis for limited position tracking quality as of when the
+  // frame was captured.
+  enum TrackingStateReason {
+    UNDEFINED_TRACKING_STATE_REASON = 0;
+    // The current tracking state is not limited.
+    NONE = 1;
+    // Not yet enough camera or motion data to provide tracking information.
+    INITIALIZING = 2;
+    // The device is moving too fast for accurate image-based position tracking.
+    EXCESSIVE_MOTION = 3;
+    // Not enough distinguishable features for image-based position tracking.
+    INSUFFICIENT_FEATURES = 4;
+    // Tracking is limited due to a relocalization in progress.
+    RELOCALIZING = 5;
+  }
+  optional TrackingStateReason tracking_state_reason = 2 [default = NONE];
+
+  // 4x4 row-major matrix expressing position and orientation of the camera in
+  // world coordinate space.
+  // See developer.apple.com/documentation/arkit/arcamera/2866108-transform for
+  // more information.
+  repeated float transform = 3 [packed = true];
+
+  // The orientation of the camera, expressed as roll, pitch, and yaw values.
+  message EulerAngles {
+    optional float roll = 1;
+    optional float pitch = 2;
+    optional float yaw = 3;
+  }
+  optional EulerAngles euler_angles = 4;
+
+  // The width and height, in pixels, of the captured camera image.
+  optional int32 image_resolution_width = 5;
+  optional int32 image_resolution_height = 6;
+
+  // 3x3 row-major matrix that converts between the 2D camera plane and 3D world
+  // coordinate space.
+  // See developer.apple.com/documentation/arkit/arcamera/2875730-intrinsics for
+  // usage information.
+  repeated float intrinsics = 7 [packed = true];
+
+  // 4x4 row-major transform matrix appropriate for rendering 3D content to
+  // match the image captured by the camera.
+  // See
+  // developer.apple.com/documentation/arkit/arcamera/2887458-projectionmatrix
+  // for usage information.
+  repeated float projection_matrix = 8 [packed = true];
+
+  // 4x4 row-major transform matrix appropriate for converting from world-space
+  // to camera space. Relativized for the captured_image orientation (i.e.
+  // UILandscapeOrientationRight).
+  // See
+  // https://developer.apple.com/documentation/arkit/arcamera/2921672-viewmatrixfororientation?language=objc
+  // for more information.
+  repeated float view_matrix = 9 [packed = true];
+}
+
+// Container for a 3D mesh describing face topology.
+message ARFaceGeometry {
+  // Each vertex represents a 3D point in the face mesh, in the face coordinate
+  // space.
+  // See developer.apple.com/documentation/arkit/arfacegeometry/2928201-vertices
+  // for more information.
+  message Vertex {
+    optional float x = 1;
+    optional float y = 2;
+    optional float z = 3;
+  }
+  repeated Vertex vertices = 1;
+
+  // The number of elements in the vertices list.
+  optional int32 vertex_count = 2;
+
+  // Each texture coordinate represents UV texture coordinates for the vertex at
+  // the corresponding index in the vertices buffer.
+  // See
+  // developer.apple.com/documentation/arkit/arfacegeometry/2928203-texturecoordinates
+  // for more information.
+  message TextureCoordinate {
+    optional float u = 1;
+    optional float v = 2;
+  }
+  repeated TextureCoordinate texture_coordinates = 3;
+
+  // The number of elements in the texture_coordinates list.
+  optional int32 texture_coordinate_count = 4;
+
+  // Each integer value in this ordered list represents an index into the
+  // vertices and texture_coordinates lists. Each set of three indices
+  // identifies the vertices comprising a single triangle in the mesh. Each set
+  // of three indices forms a triangle, so the number of indices in the
+  // triangle_indices buffer is three times the triangle_count value.
+  // See
+  // developer.apple.com/documentation/arkit/arfacegeometry/2928199-triangleindices
+  // for more information.
+  repeated int32 triangle_indices = 5 [packed = true];
+
+  // The number of triangles described by the triangle_indices buffer.
+  // See
+  // developer.apple.com/documentation/arkit/arfacegeometry/2928207-trianglecount
+  // for more information.
+  optional int32 triangle_count = 6;
+}
+
+// Contains a list of blend shape entries wherein each item maps a specific
+// blend shape location to its associated coefficient.
+message ARBlendShapeMap {
+  message MapEntry {
+    // Identifier for the specific facial feature.
+    // See developer.apple.com/documentation/arkit/arblendshapelocation for a
+    // complete list of identifiers.
+    optional string blend_shape_location = 1;
+
+    // Indicates the current position of the feature relative to its neutral
+    // configuration, ranging from 0.0 (neutral) to 1.0 (maximum movement).
+    optional float blend_shape_coefficient = 2;
+  }
+  repeated MapEntry entries = 1;
+}
+
+// Information about the pose, topology, and expression of a detected face.
+// See developer.apple.com/documentation/arkit/arfaceanchor for more info.
+message ARFaceAnchor {
+  // A coarse triangle mesh representing the topology of the detected face.
+  optional ARFaceGeometry geometry = 1;
+
+  // A map of named coefficients representing the detected facial expression in
+  // terms of the movement of specific facial features.
+  optional ARBlendShapeMap blend_shapes = 2;
+
+  // 4x4 row-major matrix encoding the position, orientation, and scale of the
+  // anchor relative to the world coordinate space.
+  // See
+  // https://developer.apple.com/documentation/arkit/aranchor/2867981-transform?language=objc
+  // for more information.
+  repeated float transform = 3;
+
+  // Indicates whether the anchor's transform is valid. Frames that have a face
+  // anchor with this value set to NO should probably be ignored.
+  optional bool is_tracked = 4;
+}
+
+// Container for a 3D mesh.
+message ARPlaneGeometry {
+  message Vertex {
+    optional float x = 1;
+    optional float y = 2;
+    optional float z = 3;
+  }
+
+  // Each texture coordinate represents UV texture coordinates for the vertex at
+  // the corresponding index in the vertices buffer.
+  // See
+  // https://developer.apple.com/documentation/arkit/arfacegeometry/2928203-texturecoordinates
+  // for more information.
+  message TextureCoordinate {
+    optional float u = 1;
+    optional float v = 2;
+  }
+
+  // A buffer of vertex positions for each point in the plane mesh.
+  repeated Vertex vertices = 1;
+
+  // The number of elements in the vertices buffer.
+  optional int32 vertex_count = 2;
+
+  // A buffer of texture coordinate values for each point in the plane mesh.
+  repeated TextureCoordinate texture_coordinates = 3;
+
+  // The number of elements in the texture_coordinates buffer.
+  optional int32 texture_coordinate_count = 4;
+
+  // Each integer value in this ordered list represents an index into the
+  // vertices and texture_coordinates lists. Each set of three indices
+  // identifies the vertices comprising a single triangle in the mesh. Each set
+  // of three indices forms a triangle, so the number of indices in the
+  // triangle_indices buffer is three times the triangle_count value.
+  // See
+  // https://developer.apple.com/documentation/arkit/arplanegeometry/2941051-triangleindices
+  // for more information.
+  repeated int32 triangle_indices = 5 [packed = true];
+
+  // Each set of three indices forms a triangle, so the number of indices in the
+  // triangle_indices buffer is three times the triangle_count value.
+  // See
+  // https://developer.apple.com/documentation/arkit/arplanegeometry/2941058-trianglecount
+  // for more information.
+  optional int32 triangle_count = 6;
+
+  // Each value in this buffer represents the position of a vertex along the
+  // boundary polygon of the estimated plane. The owning plane anchor's
+  // transform matrix defines the coordinate system for these points.
+  // See
+  // https://developer.apple.com/documentation/arkit/arplanegeometry/2941052-boundaryvertices
+  // for more information.
+  repeated Vertex boundary_vertices = 7;
+
+  // The number of elements in the boundary_vertices buffer.
+  optional int32 boundary_vertex_count = 8;
+}
+
+// Information about the position and orientation of a real-world flat surface.
+// See https://developer.apple.com/documentation/arkit/arplaneanchor for more
+// information.
+message ARPlaneAnchor {
+  enum Alignment {
+    UNDEFINED = 0;
+    // The plane is perpendicular to gravity.
+    HORIZONTAL = 1;
+    // The plane is parallel to gravity.
+    VERTICAL = 2;
+  }
+
+  // Wrapper for a 3D point / vector within the plane. See extent and center
+  // values for more information.
+  message PlaneVector {
+    optional float x = 1;
+    optional float y = 2;
+    optional float z = 3;
+  }
+
+  enum PlaneClassification {
+    NONE = 0;
+    WALL = 1;
+    FLOOR = 2;
+    CEILING = 3;
+    TABLE = 4;
+    SEAT = 5;
+  }
+
+  // The classification status for the plane.
+  enum PlaneClassificationStatus {
+    // The classfication process for the plane anchor has completed but the
+    // result is inconclusive.
+    UNKNOWN = 0;
+    // No classication information can be provided (set on error or if the
+    // device does not support plane classification).
+    UNAVAILABLE = 1;
+    // The classification process has not completed.
+    UNDETERMINED = 2;
+    // The classfication process for the plane anchor has completed.
+    KNOWN = 3;
+  }
+
+  // The ID of the plane.
+  optional string identifier = 1;
+
+  // 4x4 row-major matrix encoding the position, orientation, and scale of the
+  // anchor relative to the world coordinate space.
+  // See
+  // https://developer.apple.com/documentation/arkit/aranchor/2867981-transform
+  // for more information.
+  repeated float transform = 2;
+
+  // The general orientation of the detected plane with respect to gravity.
+  optional Alignment alignment = 3;
+
+  // A coarse triangle mesh representing the general shape of the detected
+  // plane.
+  optional ARPlaneGeometry geometry = 4;
+
+  // The center point of the plane relative to its anchor position.
+  // Although the type of this property is a 3D vector, a plane anchor is always
+  // two-dimensional, and is always positioned in only the x and z directions
+  // relative to its transform position. (That is, the y-component of this
+  // vector is always zero.)
+  // See
+  // https://developer.apple.com/documentation/arkit/arplaneanchor/2882056-center
+  // for more information.
+  optional PlaneVector center = 5;
+
+  // The estimated width and length of the detected plane.
+  // See
+  // https://developer.apple.com/documentation/arkit/arplaneanchor/2882055-extent
+  // for more information.
+  optional PlaneVector extent = 6;
+
+  // A Boolean value that indicates whether plane classification is available on
+  // the current device. On devices without plane classification support, all
+  // plane anchors report a classification value of NONE
+  // and a classification_status value of UNAVAILABLE.
+  optional bool classification_supported = 7;
+
+  // A general characterization of what kind of real-world surface the plane
+  // anchor represents.
+  // See
+  // https://developer.apple.com/documentation/arkit/arplaneanchor/2990936-classification
+  // for more information.
+  optional PlaneClassification classification = 8;
+
+  // The current state of ARKit's process for classifying the plane anchor.
+  // When this property's value is KNOWN, the classification property represents
+  // ARKit's characterization of the real-world surface corresponding to the
+  // plane anchor.
+  // See
+  // https://developer.apple.com/documentation/arkit/arplaneanchor/2990937-classificationstatus
+  // for more information.
+  optional PlaneClassificationStatus classification_status = 9;
+}
+
+// A collection of points in the world coordinate space.
+// See https://developer.apple.com/documentation/arkit/arpointcloud for more
+// information.
+message ARPointCloud {
+  message Point {
+    optional float x = 1;
+    optional float y = 2;
+    optional float z = 3;
+  }
+
+  // The number of points in the cloud.
+  optional int32 count = 1;
+
+  // The list of detected points.
+  repeated Point point = 2;
+
+  // A list of unique identifiers corresponding to detected feature points.
+  // Each identifier in this list corresponds to the point at the same index
+  // in the points array.
+  repeated int64 identifier = 3 [packed = true];
+}
+
+// Video image and face position tracking information.
+// See developer.apple.com/documentation/arkit/arframe for more information.
+message ARFrame {
+  // The timestamp for the frame.
+  optional double timestamp = 1;
+
+  // The depth data associated with the frame. Not all frames have depth data.
+  optional AVDepthData depth_data = 2;
+
+  // The depth data object timestamp associated with the frame. May differ from
+  // the frame timestamp value. Is only set when the frame has depth_data.
+  optional double depth_data_timestamp = 3;
+
+  // Camera information associated with the frame.
+  optional ARCamera camera = 4;
+
+  // Light information associated with the frame.
+  optional ARLightEstimate light_estimate = 5;
+
+  // Face anchor information associated with the frame. Not all frames have an
+  // active face anchor.
+  optional ARFaceAnchor face_anchor = 6;
+
+  // Plane anchors associated with the frame. Not all frames have a plane
+  // anchor. Plane anchors and face anchors are mutually exclusive.
+  repeated ARPlaneAnchor plane_anchor = 7;
+
+  // The current intermediate results of the scene analysis used to perform
+  // world tracking.
+  // See
+  // https://developer.apple.com/documentation/arkit/arframe/2887449-rawfeaturepoints
+  // for more information.
+  optional ARPointCloud raw_feature_points = 8;
+}
diff --git a/mediapipe/modules/objectron/calculators/annotation_data.proto b/mediapipe/modules/objectron/calculators/annotation_data.proto
new file mode 100644
index 0000000..6c26d29
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/annotation_data.proto
@@ -0,0 +1,108 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto3";
+
+package mediapipe;
+
+import "mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto";
+import "mediapipe/modules/objectron/calculators/object.proto";
+
+// Projection of a 3D point on an image, and its metric depth.
+message NormalizedPoint2D {
+  // x-y position of the 2d keypoint in the image coordinate system.
+  // u,v \in [0, 1], where top left corner is (0, 0) and the bottom-right corner
+  // is (1, 1).
+  float x = 1;
+  float y = 2;
+
+  // The depth of the point in the camera coordinate system (in meters).
+  float depth = 3;
+}
+
+// The 3D point in the camera coordinate system, the scales are in meters.
+message Point3D {
+  float x = 1;
+  float y = 2;
+  float z = 3;
+}
+
+message AnnotatedKeyPoint {
+  int32 id = 1;
+  Point3D point_3d = 2;
+  NormalizedPoint2D point_2d = 3;
+  // Indicates whether this keypoint is hidden or not. The hidden attribute is
+  // determined from the object's skeleton. For box model, none of the keypoints
+  // are hidden.
+  bool hidden = 4;
+}
+
+message ObjectAnnotation {
+  // Reference to the object identifier in ObjectInstance.
+  int32 object_id = 1;
+
+  // For each objects, list all the annotated keypoints here.
+  // E.g. for bounding-boxes, we have 8 keypoints, hands = 21 keypoints, etc.
+  // These normalized points are the projection of the Object's 3D keypoint
+  // on the current frame's camera poses.
+  repeated AnnotatedKeyPoint keypoints = 2;
+
+  // Visibiity of this annotation in a frame.
+  float visibility = 3;
+
+  // 3x3 row-major rotation matrix describing the orientation of the rigid
+  // object's frame of reference in the camera-coordinate system.
+  repeated float rotation = 4;
+
+  // 3x1 vector describing the translation of the rigid object's frame of
+  // reference in the camera-coordinate system in meters.
+  repeated float translation = 5;
+
+  // 3x1 vector describing the scale of the rigid object's frame of reference in
+  // the camera-coordinate system.
+  repeated float scale = 6;
+}
+
+message FrameAnnotation {
+  // Unique frame id, corresponds to images.
+  int32 frame_id = 1;
+
+  // List of the annotated objects in this frame. Depending on how many object
+  // are observable in this frame, we might have non or as much as
+  // sequence.objects_size() annotations.
+  repeated ObjectAnnotation annotations = 2;
+
+  // Information about the camera transformation (in the world coordinate) and
+  // imaging characteristics for a captured video frame.
+  ARCamera camera = 3;
+
+  // The timestamp for the frame.
+  double timestamp = 4;
+
+  // Plane center and normal in camera frame.
+  repeated float plane_center = 5;
+  repeated float plane_normal = 6;
+}
+
+// The sequence protocol contains the annotation data for the entire video clip.
+message Sequence {
+  // List of all the annotated 3D objects in this sequence in the world
+  // Coordinate system. Given the camera poses of each frame (also in the
+  // world-coordinate) these objects bounding boxes can be projected to each
+  // frame to get the per-frame annotation (i.e. image_annotation below).
+  repeated Object objects = 1;
+
+  // List of annotated data per each frame in sequence + frame information.
+  repeated FrameAnnotation frame_annotations = 2;
+}
diff --git a/mediapipe/modules/objectron/calculators/belief_decoder_config.proto b/mediapipe/modules/objectron/calculators/belief_decoder_config.proto
new file mode 100644
index 0000000..f0f10ae
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/belief_decoder_config.proto
@@ -0,0 +1,38 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+message BeliefDecoderConfig {
+  optional float heatmap_threshold = 1 [default = 0.9];
+  // Maximum distance in pixels between two local max heatmap values.
+  optional float local_max_distance = 2 [default = 10.0];
+  // Coefficient of offset_scale.
+  // offset_scale = offset_scale_coef * min(rows, cols).
+  // offset_scale is used to multiply the offset predictions from the network.
+  optional float offset_scale_coef = 3 [default = 0.5, deprecated = true];
+
+  // The radius for vertex voting. Use no voting if the radius is less than or
+  // euqal to 1. Example: 10.
+  optional int32 voting_radius = 4;
+
+  // The number of pixels to determine whether two points are the same.
+  // Example: 5 (voting_radius / 2).
+  optional int32 voting_allowance = 5;
+
+  // The threshold of beliefs, with which the points can vote. Example: 0.2.
+  optional float voting_threshold = 6;
+}
diff --git a/mediapipe/modules/objectron/calculators/box.cc b/mediapipe/modules/objectron/calculators/box.cc
new file mode 100644
index 0000000..bd2ce57
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/box.cc
@@ -0,0 +1,255 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/box.h"
+
+#include "Eigen/Core"
+#include "mediapipe/framework/port/logging.h"
+
+namespace mediapipe {
+
+namespace {
+constexpr int kFrontFaceId = 4;
+constexpr int kTopFaceId = 2;
+constexpr int kNumKeypoints = 8 + 1;
+constexpr int kNumberOfAxis = 3;
+constexpr int kEdgesPerAxis = 4;
+
+}  // namespace
+
+Box::Box(const std::string& category)
+    : Model(kBoundingBox, kNumKeypoints, category),
+      bounding_box_(kNumKeypoints) {
+  transformation_.setIdentity();
+
+  scale_ << 0.1, 0.1, 0.1;
+
+  // The vertices are ordered according to the left-hand rule, so the normal
+  // vector of each face will point inward the box.
+  faces_.push_back({5, 6, 8, 7});  // +x on yz plane
+  faces_.push_back({1, 3, 4, 2});  // -x on yz plane
+
+  faces_.push_back({3, 7, 8, 4});  // +y on xz plane = top
+  faces_.push_back({1, 2, 6, 5});  // -y on xz plane
+
+  faces_.push_back({2, 4, 8, 6});  // +z on xy plane = front
+  faces_.push_back({1, 5, 7, 3});  // -z on xy plane
+
+  // Add the edges in the cube, they are sorted according to axis (x-y-z).
+  edges_.push_back({1, 5});
+  edges_.push_back({2, 6});
+  edges_.push_back({3, 7});
+  edges_.push_back({4, 8});
+
+  edges_.push_back({1, 3});
+  edges_.push_back({5, 7});
+  edges_.push_back({2, 4});
+  edges_.push_back({6, 8});
+
+  edges_.push_back({1, 2});
+  edges_.push_back({3, 4});
+  edges_.push_back({5, 6});
+  edges_.push_back({7, 8});
+  Update();
+}
+
+void Box::Update() {
+  // Compute the eight vertices of the bounding box from Box's parameters
+  auto w = scale_[0] / 2.f;
+  auto h = scale_[1] / 2.f;
+  auto d = scale_[2] / 2.f;
+
+  // Define the local coordinate system, w.r.t. the center of the boxs
+  bounding_box_[0] << 0., 0., 0.;
+  bounding_box_[1] << -w, -h, -d;
+  bounding_box_[2] << -w, -h, +d;
+  bounding_box_[3] << -w, +h, -d;
+  bounding_box_[4] << -w, +h, +d;
+  bounding_box_[5] << +w, -h, -d;
+  bounding_box_[6] << +w, -h, +d;
+  bounding_box_[7] << +w, +h, -d;
+  bounding_box_[8] << +w, +h, +d;
+
+  // Convert to world coordinate system
+  for (int i = 0; i < kNumKeypoints; ++i) {
+    bounding_box_[i] =
+        transformation_.topLeftCorner<3, 3>() * bounding_box_[i] +
+        transformation_.col(3).head<3>();
+  }
+}
+
+void Box::Adjust(const std::vector<float>& variables) {
+  Eigen::Vector3f translation;
+  translation << variables[0], variables[1], variables[2];
+  SetTranslation(translation);
+
+  const float roll = variables[3];
+  const float pitch = variables[4];
+  const float yaw = variables[5];
+  SetRotation(roll, pitch, yaw);
+
+  Eigen::Vector3f scale;
+  scale << variables[6], variables[7], variables[8];
+
+  SetScale(scale);
+  Update();
+}
+
+float* Box::GetVertex(size_t vertex_id) {
+  CHECK_LT(vertex_id, kNumKeypoints);
+  return bounding_box_[vertex_id].data();
+}
+
+const float* Box::GetVertex(size_t vertex_id) const {
+  CHECK_LT(vertex_id, kNumKeypoints);
+  return bounding_box_[vertex_id].data();
+}
+
+bool Box::InsideTest(const Eigen::Vector3f& point, int check_axis) const {
+  const float* v0 = GetVertex(1);
+  const float* v1 = GetVertex(2);
+  const float* v2 = GetVertex(3);
+  const float* v4 = GetVertex(5);
+
+  switch (check_axis) {
+    case 1:
+      return (v0[0] <= point[0] && point[0] <= v1[0]);  // X-axis
+    case 2:
+      return (v0[1] <= point[1] && point[1] <= v2[1]);  // Y-axis
+    case 3:
+      return (v0[2] <= point[2] && point[2] <= v4[2]);  // Z-axis
+    default:
+      return false;
+  }
+}
+
+void Box::Deserialize(const Object& obj) {
+  CHECK_EQ(obj.keypoints_size(), kNumKeypoints);
+  Model::Deserialize(obj);
+}
+
+void Box::Serialize(Object* obj) {
+  Model::Serialize(obj);
+  obj->set_type(Object::BOUNDING_BOX);
+  std::vector<Vector3f> local_bounding_box(9);
+  // Define the local coordinate system, w.r.t. the center of the boxs
+  local_bounding_box[0] << 0., 0., 0.;
+  local_bounding_box[1] << -0.5, -0.5, -0.5;
+  local_bounding_box[2] << -0.5, -0.5, +0.5;
+  local_bounding_box[3] << -0.5, +0.5, -0.5;
+  local_bounding_box[4] << -0.5, +0.5, +0.5;
+  local_bounding_box[5] << +0.5, -0.5, -0.5;
+  local_bounding_box[6] << +0.5, -0.5, +0.5;
+  local_bounding_box[7] << +0.5, +0.5, -0.5;
+  local_bounding_box[8] << +0.5, +0.5, +0.5;
+  for (int i = 0; i < kNumKeypoints; ++i) {
+    KeyPoint* keypoint = obj->add_keypoints();
+    keypoint->set_x(local_bounding_box[i][0]);
+    keypoint->set_y(local_bounding_box[i][1]);
+    keypoint->set_z(local_bounding_box[i][2]);
+    keypoint->set_confidence_radius(0.);
+  }
+}
+
+const Face& Box::GetFrontFace() const { return faces_[kFrontFaceId]; }
+
+const Face& Box::GetTopFace() const { return faces_[kTopFaceId]; }
+
+std::pair<Vector3f, Vector3f> Box::GetGroundPlane() const {
+  const Vector3f gravity = Vector3f(0., 1., 0.);
+  int ground_plane_id = 0;
+  float ground_plane_error = 10.0;
+
+  auto get_face_center = [&](const Face& face) {
+    Vector3f center = Vector3f::Zero();
+    for (const int vertex_id : face) {
+      center += Map<const Vector3f>(GetVertex(vertex_id));
+    }
+    center /= face.size();
+    return center;
+  };
+
+  auto get_face_normal = [&](const Face& face, const Vector3f& center) {
+    Vector3f v1 = Map<const Vector3f>(GetVertex(face[0])) - center;
+    Vector3f v2 = Map<const Vector3f>(GetVertex(face[1])) - center;
+    Vector3f normal = v1.cross(v2);
+    return normal;
+  };
+
+  // The ground plane is defined as a plane aligned with gravity.
+  // gravity is the (0, 1, 0) vector in the world coordinate system.
+  const auto& faces = GetFaces();
+  for (int face_id = 0; face_id < faces.size(); face_id += 2) {
+    const auto& face = faces[face_id];
+    Vector3f center = get_face_center(face);
+    Vector3f normal = get_face_normal(face, center);
+    Vector3f w = gravity.cross(normal);
+    const float w_sq_norm = w.squaredNorm();
+    if (w_sq_norm < ground_plane_error) {
+      ground_plane_error = w_sq_norm;
+      ground_plane_id = face_id;
+    }
+  }
+
+  Vector3f center = get_face_center(faces[ground_plane_id]);
+  Vector3f normal = get_face_normal(faces[ground_plane_id], center);
+
+  // For each face, we also have a parallel face that it's normal is also
+  // aligned with gravity vector. We pick the face with lower height (y-value).
+  // The parallel to face 0 is 1, face 2 is 3, and face 4 is 5.
+  int parallel_face_id = ground_plane_id + 1;
+  const auto& parallel_face = faces[parallel_face_id];
+  Vector3f parallel_face_center = get_face_center(parallel_face);
+  Vector3f parallel_face_normal =
+      get_face_normal(parallel_face, parallel_face_center);
+  if (parallel_face_center[1] < center[1]) {
+    center = parallel_face_center;
+    normal = parallel_face_normal;
+  }
+  return {center, normal};
+}
+
+template <typename T>
+void Box::Fit(const std::vector<T>& vertices) {
+  CHECK_EQ(vertices.size(), kNumKeypoints);
+  scale_.setZero();
+  // The scale would remain invariant under rotation and translation.
+  // We can safely estimate the scale from the oriented box.
+  for (int axis = 0; axis < kNumberOfAxis; ++axis) {
+    for (int edge_id = 0; edge_id < kEdgesPerAxis; ++edge_id) {
+      // The edges are stored in quadruples according to each axis
+      const std::array<int, 2>& edge = edges_[axis * kEdgesPerAxis + edge_id];
+      scale_[axis] += (vertices[edge[0]] - vertices[edge[1]]).norm();
+    }
+    scale_[axis] /= kEdgesPerAxis;
+  }
+  // Create a scaled axis-aligned box
+  transformation_.setIdentity();
+  Update();
+
+  using MatrixN3_RM = Eigen::Matrix<float, kNumKeypoints, 3, Eigen::RowMajor>;
+  Eigen::Map<const MatrixN3_RM> v(vertices[0].data());
+  Eigen::Map<const MatrixN3_RM> system(bounding_box_[0].data());
+  auto system_h = system.rowwise().homogeneous().eval();
+  auto system_g = system_h.colPivHouseholderQr();
+  auto solution = system_g.solve(v).eval();
+  transformation_.topLeftCorner<3, 4>() = solution.transpose();
+  Update();
+}
+
+template void Box::Fit<Vector3f>(const std::vector<Vector3f>&);
+template void Box::Fit<Map<Vector3f>>(const std::vector<Map<Vector3f>>&);
+template void Box::Fit<Map<const Vector3f>>(
+    const std::vector<Map<const Vector3f>>&);
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/box.h b/mediapipe/modules/objectron/calculators/box.h
new file mode 100644
index 0000000..17218f7
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/box.h
@@ -0,0 +1,132 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_H_
+
+#include <vector>
+
+#include "mediapipe/modules/objectron/calculators/model.h"
+
+namespace mediapipe {
+
+// Model for the bounding box in 3D
+// The box has 9 degrees of freedom, which uniquely defines 8 keypoints in the
+// fixed world-coordinate system.
+//
+// The 8 keypoints are defined as follows
+//
+//  kp-id  axis
+//  0      000    ---
+//  1      001    --+
+//  2      010    -+-
+//  3      011    -++
+//  4      100    +--
+//  5      101    +-+
+//  6      110    ++-
+//  7      111    +++
+//
+// where xyz means positive or negative vector along the axis where the center
+// of the box is the origin. The resulting bounding box is
+//
+//              x                              x
+//      0 + + + + + + + + 4                 .-------
+//      +\                +\                |\
+//      + \ y             + \             z | \ y
+//      +  \              +  \              |  \
+//      +   2 + + + + + + + + 6
+//    z +   +             +   +
+//      +   +             +   +
+//      +   +     C       +   +
+//      +   +             +   +
+//      1 + + + + + + + + 5   +
+//       \  +              \  +
+//        \ +               \ +
+//         \+                \+
+//          3 + + + + + + + + 7
+//
+// World coordinate system: +y is up (aligned with gravity),
+// +z is toward the user, +x follows right hand rule.
+// The front face is defined as +z axis on xy plane.
+// The top face is defined as +y axis on xz plane.
+//
+
+class Box : public Model {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
+
+  explicit Box(const std::string& category);
+  ~Box() override = default;
+
+  bool InsideTest(const Vector3f& point, int check_axis) const;
+
+  const std::vector<Face>& GetFaces() const { return faces_; }
+  const Face& GetFace(size_t face_id) const { return faces_[face_id]; }
+
+  const std::vector<std::array<int, 2>>& GetEdges() const { return edges_; }
+  const std::array<int, 2>& GetEdge(size_t edge_id) const {
+    return edges_[edge_id];
+  }
+
+  // Returns the keypoints for the front face of the box.
+  // The front face is defind as a face with +z normal vector on xy plane
+  // In Box's c'tor, the top face is set to {1, 3, 7, 5}
+  const Face& GetFrontFace() const;
+
+  // Returns the keypoints for the top face of the box.
+  // The top face is defind as a face with +z normal vector on xy plane
+  // In Box's c'tor, the top face is set to {1, 3, 7, 5}
+  const Face& GetTopFace() const;
+
+  void Update() override;
+  void Adjust(const std::vector<float>& variables) override;
+  float* GetVertex(size_t vertex_id) override;
+  const float* GetVertex(size_t vertex_id) const override;
+  void Deserialize(const Object& obj) override;
+  void Serialize(Object* obj) override;
+
+  // Computes the plane center and the normal vector for the plane the object
+  // is sitting on in the world cooordinate system. The normal vector is roughly
+  // aligned with gravity.
+  std::pair<Vector3f, Vector3f> GetGroundPlane() const;
+
+  // Estimates a box 9-dof parameters from the given vertices. Directly computes
+  // the scale of the box, then solves for orientation and translation.
+  // Expects a std::vector of size 9 of a Eigen::Vector3f or mapped Vector3f.
+  // If mapping proto messages, we recommend to use the Map<const Vector3f>.
+  // For example:
+  //
+  // using T = Map<const Vector3f>;
+  // std::vector<T> vertices;
+  // for (const auto& point : message) { // point is a repeated float message.
+  //   T p(point.data());
+  //   vertices.emplace_back(p);
+  // }
+  // box.Fit<T>(vertices);
+  //
+  // The Points must be arranged as 1 + 8 (center keypoint followed by 8 box
+  // vertices) vector. This function will overwrite the scale and transformation
+  // properties of the class.
+  template <typename T = Eigen::Map<const Vector3f>>
+  void Fit(const std::vector<T>& vertices);
+
+ private:
+  std::vector<Face> faces_;
+  std::vector<std::array<int, 2>> edges_;
+  std::vector<Vector3f> bounding_box_;
+};
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_H_
diff --git a/mediapipe/modules/objectron/calculators/box_util.cc b/mediapipe/modules/objectron/calculators/box_util.cc
new file mode 100644
index 0000000..0663b5b
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/box_util.cc
@@ -0,0 +1,153 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/box_util.h"
+
+#include <math.h>
+
+#include "mediapipe/framework/port/logging.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/framework/port/opencv_imgproc_inc.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace mediapipe {
+void ComputeBoundingRect(const std::vector<cv::Point2f>& points,
+                         mediapipe::TimedBoxProto* box) {
+  CHECK(box != nullptr);
+  float top = 1.0f;
+  float bottom = 0.0f;
+  float left = 1.0f;
+  float right = 0.0f;
+  for (const auto& point : points) {
+    top = std::min(top, point.y);
+    bottom = std::max(bottom, point.y);
+    left = std::min(left, point.x);
+    right = std::max(right, point.x);
+  }
+  box->set_top(top);
+  box->set_bottom(bottom);
+  box->set_left(left);
+  box->set_right(right);
+  // We are currently only doing axis aligned bounding box. If we need to
+  // compute rotated bounding box, then we need the original image aspect ratio,
+  // map back to original image space, compute cv::convexHull, then for each
+  // edge of the hull, rotate according to edge orientation, find the box.
+  box->set_rotation(0.0f);
+}
+
+float ComputeBoxIoU(const TimedBoxProto& box1, const TimedBoxProto& box2) {
+  cv::Point2f box1_center((box1.left() + box1.right()) * 0.5f,
+                          (box1.top() + box1.bottom()) * 0.5f);
+  cv::Size2f box1_size(box1.right() - box1.left(), box1.bottom() - box1.top());
+  cv::RotatedRect rect1(box1_center, box1_size,
+                        -box1.rotation() * 180.0f / M_PI);
+  cv::Point2f box2_center((box2.left() + box2.right()) * 0.5f,
+                          (box2.top() + box2.bottom()) * 0.5f);
+  cv::Size2f box2_size(box2.right() - box2.left(), box2.bottom() - box2.top());
+  cv::RotatedRect rect2(box2_center, box2_size,
+                        -box2.rotation() * 180.0f / M_PI);
+  std::vector<cv::Point2f> intersections_unsorted;
+  std::vector<cv::Point2f> intersections;
+  cv::rotatedRectangleIntersection(rect1, rect2, intersections_unsorted);
+  if (intersections_unsorted.size() < 3) {
+    return 0.0f;
+  }
+  cv::convexHull(intersections_unsorted, intersections);
+
+  // We use Shoelace formula to compute area of polygons.
+  float intersection_area = 0.0f;
+  for (int i = 0; i < intersections.size(); ++i) {
+    const auto& curr_pt = intersections[i];
+    const int i_next = (i + 1) == intersections.size() ? 0 : (i + 1);
+    const auto& next_pt = intersections[i_next];
+    intersection_area += (curr_pt.x * next_pt.y - next_pt.x * curr_pt.y);
+  }
+  intersection_area = std::abs(intersection_area) * 0.5f;
+
+  // Compute union area
+  const float union_area =
+      rect1.size.area() + rect2.size.area() - intersection_area + 1e-5f;
+
+  const float iou = intersection_area / union_area;
+  return iou;
+}
+
+std::vector<cv::Point2f> ComputeBoxCorners(const TimedBoxProto& box,
+                                           float width, float height) {
+  // Rotate 4 corner w.r.t. center.
+  const cv::Point2f center(0.5f * (box.left() + box.right()) * width,
+                           0.5f * (box.top() + box.bottom()) * height);
+  const std::vector<cv::Point2f> corners{
+      cv::Point2f(box.left() * width, box.top() * height),
+      cv::Point2f(box.left() * width, box.bottom() * height),
+      cv::Point2f(box.right() * width, box.bottom() * height),
+      cv::Point2f(box.right() * width, box.top() * height)};
+
+  const float cos_a = std::cos(box.rotation());
+  const float sin_a = std::sin(box.rotation());
+  std::vector<cv::Point2f> transformed_corners(4);
+  for (int k = 0; k < 4; ++k) {
+    // Scale and rotate w.r.t. center.
+    const cv::Point2f rad = corners[k] - center;
+    const cv::Point2f rot_rad(cos_a * rad.x - sin_a * rad.y,
+                              sin_a * rad.x + cos_a * rad.y);
+    transformed_corners[k] = center + rot_rad;
+    transformed_corners[k].x /= width;
+    transformed_corners[k].y /= height;
+  }
+  return transformed_corners;
+}
+
+cv::Mat PerspectiveTransformBetweenBoxes(const TimedBoxProto& src_box,
+                                         const TimedBoxProto& dst_box,
+                                         const float aspect_ratio) {
+  std::vector<cv::Point2f> box1_corners =
+      ComputeBoxCorners(src_box, /*width*/ aspect_ratio, /*height*/ 1.0f);
+  std::vector<cv::Point2f> box2_corners =
+      ComputeBoxCorners(dst_box, /*width*/ aspect_ratio, /*height*/ 1.0f);
+  cv::Mat affine_transform = cv::getPerspectiveTransform(
+      /*src*/ box1_corners, /*dst*/ box2_corners);
+  cv::Mat output_affine;
+  affine_transform.convertTo(output_affine, CV_32FC1);
+  return output_affine;
+}
+
+cv::Point2f MapPoint(const TimedBoxProto& src_box, const TimedBoxProto& dst_box,
+                     const cv::Point2f& src_point, float width, float height) {
+  const cv::Point2f src_center(
+      0.5f * (src_box.left() + src_box.right()) * width,
+      0.5f * (src_box.top() + src_box.bottom()) * height);
+  const cv::Point2f dst_center(
+      0.5f * (dst_box.left() + dst_box.right()) * width,
+      0.5f * (dst_box.top() + dst_box.bottom()) * height);
+  const float scale_x =
+      (dst_box.right() - dst_box.left()) / (src_box.right() - src_box.left());
+  const float scale_y =
+      (dst_box.bottom() - dst_box.top()) / (src_box.bottom() - src_box.top());
+  const float rotation = dst_box.rotation() - src_box.rotation();
+  const cv::Point2f rad =
+      cv::Point2f(src_point.x * width, src_point.y * height) - src_center;
+  const float rad_x = rad.x * scale_x;
+  const float rad_y = rad.y * scale_y;
+  const float cos_a = std::cos(rotation);
+  const float sin_a = std::sin(rotation);
+  const cv::Point2f rot_rad(cos_a * rad_x - sin_a * rad_y,
+                            sin_a * rad_x + cos_a * rad_y);
+  const cv::Point2f dst_point_image = dst_center + rot_rad;
+  const cv::Point2f dst_point(dst_point_image.x / width,
+                              dst_point_image.y / height);
+  return dst_point;
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/box_util.h b/mediapipe/modules/objectron/calculators/box_util.h
new file mode 100644
index 0000000..fed21c0
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/box_util.h
@@ -0,0 +1,50 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_UTIL_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_UTIL_H_
+
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace mediapipe {
+
+// This function fills the geometry of the TimedBoxProto. Id, timestamp etc.
+// need to be set outside this function.
+void ComputeBoundingRect(const std::vector<cv::Point2f>& points,
+                         mediapipe::TimedBoxProto* box);
+
+// This function computes the intersection over union between two boxes.
+float ComputeBoxIoU(const TimedBoxProto& box1, const TimedBoxProto& box2);
+
+// Computes corners of the box.
+// width and height are image width and height, which is typically
+// needed since the box is in normalized coordinates.
+std::vector<cv::Point2f> ComputeBoxCorners(const TimedBoxProto& box,
+                                           float width, float height);
+
+// Computes the perspective transform from box1 to box2.
+// The input argument aspect_ratio is width / height of the image.
+// The returned matrix should be a 3x3 matrix.
+cv::Mat PerspectiveTransformBetweenBoxes(const TimedBoxProto& src_box,
+                                         const TimedBoxProto& dst_box,
+                                         const float aspect_ratio);
+
+// Map point according to source and destination box location.
+cv::Point2f MapPoint(const TimedBoxProto& src_box, const TimedBoxProto& dst_box,
+                     const cv::Point2f& src_point, float width, float height);
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_UTIL_H_
diff --git a/mediapipe/modules/objectron/calculators/box_util_test.cc b/mediapipe/modules/objectron/calculators/box_util_test.cc
new file mode 100644
index 0000000..2a3895f
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/box_util_test.cc
@@ -0,0 +1,123 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/box_util.h"
+
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace mediapipe {
+namespace {
+
+TEST(BoxUtilTest, TestComputeBoundingRect) {
+  std::vector<cv::Point2f> points{
+      cv::Point2f(0.35f, 0.25f), cv::Point2f(0.3f, 0.3f),
+      cv::Point2f(0.2f, 0.4f),   cv::Point2f(0.3f, 0.1f),
+      cv::Point2f(0.2f, 0.2f),   cv::Point2f(0.5f, 0.3f),
+      cv::Point2f(0.4f, 0.4f),   cv::Point2f(0.5f, 0.1f),
+      cv::Point2f(0.4f, 0.2f)};
+  TimedBoxProto box;
+  ComputeBoundingRect(points, &box);
+  EXPECT_FLOAT_EQ(0.1f, box.top());
+  EXPECT_FLOAT_EQ(0.4f, box.bottom());
+  EXPECT_FLOAT_EQ(0.2f, box.left());
+  EXPECT_FLOAT_EQ(0.5f, box.right());
+}
+
+TEST(BoxUtilTest, TestComputeBoxIoU) {
+  TimedBoxProto box1;
+  box1.set_top(0.2f);
+  box1.set_bottom(0.6f);
+  box1.set_left(0.1f);
+  box1.set_right(0.3f);
+  box1.set_rotation(0.0f);
+  TimedBoxProto box2 = box1;
+  box2.set_rotation(/*pi/2*/ 1.570796f);
+  const float box_area =
+      (box1.bottom() - box1.top()) * (box1.right() - box1.left());
+  const float box_intersection =
+      (box1.right() - box1.left()) * (box1.right() - box1.left());
+  const float expected_iou =
+      box_intersection / (box_area * 2 - box_intersection);
+  EXPECT_NEAR(expected_iou, ComputeBoxIoU(box1, box2), 3e-5f);
+
+  TimedBoxProto box3;
+  box3.set_top(0.2f);
+  box3.set_bottom(0.6f);
+  box3.set_left(0.5f);
+  box3.set_right(0.7f);
+  EXPECT_NEAR(0.0f, ComputeBoxIoU(box1, box3), 3e-5f);
+}
+
+TEST(BoxUtilTest, TestPerspectiveTransformBetweenBoxes) {
+  TimedBoxProto box1;
+  const float height = 4.0f;
+  const float width = 3.0f;
+  box1.set_top(1.0f / height);
+  box1.set_bottom(2.0f / height);
+  box1.set_left(1.0f / width);
+  box1.set_right(2.0f / width);
+  TimedBoxProto box2;
+  box2.set_top(1.0f / height);
+  box2.set_bottom(2.0f / height);
+  box2.set_left(1.0f / width);
+  box2.set_right(2.0f / width);
+  box2.set_rotation(/*pi/4*/ -0.785398f);
+  cv::Mat transform =
+      PerspectiveTransformBetweenBoxes(box1, box2, width / height);
+  const float kTolerence = 1e-5f;
+  const cv::Vec3f original_position(1.5f / width, 1.0f / height, 1.0f);
+  const cv::Mat transformed_position = transform * cv::Mat(original_position);
+  EXPECT_NEAR(
+      (1.5f - 0.5f * std::sqrt(2) / 2.0f) / width,
+      transformed_position.at<float>(0) / transformed_position.at<float>(2),
+      kTolerence);
+  EXPECT_NEAR(
+      (1.5f - 0.5f * std::sqrt(2) / 2.0f) / height,
+      transformed_position.at<float>(1) / transformed_position.at<float>(2),
+      kTolerence);
+}
+
+TEST(BoxUtilTest, TestMapPoint) {
+  const float height = 4.0f;
+  const float width = 3.0f;
+  TimedBoxProto box1;
+  box1.set_top(1.0f / height);
+  box1.set_bottom(2.0f / height);
+  box1.set_left(1.0f / width);
+  box1.set_right(2.0f / width);
+  TimedBoxProto box2;
+  box2.set_top(1.0f / height);
+  box2.set_bottom(2.0f / height);
+  box2.set_left(1.0f / width);
+  box2.set_right(2.0f / width);
+  box2.set_rotation(/*pi/4*/ -0.785398f);
+
+  cv::Point2f src_point1(1.2f / width, 1.4f / height);
+  cv::Point2f src_point2(1.3f / width, 1.8f / height);
+  const float distance1 = std::sqrt(0.1 * 0.1 + 0.4 * 0.4);
+  cv::Point2f dst_point1 = MapPoint(box1, box2, src_point1, width, height);
+  cv::Point2f dst_point2 = MapPoint(box1, box2, src_point2, width, height);
+  const float distance2 =
+      std::sqrt((dst_point1.x * width - dst_point2.x * width) *
+                    (dst_point1.x * width - dst_point2.x * width) +
+                (dst_point1.y * height - dst_point2.y * height) *
+                    (dst_point1.y * height - dst_point2.y * height));
+  EXPECT_NEAR(distance1, distance2, 1e-5f);
+}
+
+}  // namespace
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/camera_parameters.proto b/mediapipe/modules/objectron/calculators/camera_parameters.proto
new file mode 100644
index 0000000..f5c843b
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/camera_parameters.proto
@@ -0,0 +1,47 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+message CameraParametersProto {
+  // This number is non-negative, it represents camera height above ground
+  // normalized by focal length.
+  optional float height_above_ground = 1 [default = 100.0];
+  // Width of image in portrait orientation normalized by focal length
+  optional float portrait_width = 2 [default = 1.0103];
+  // Height of image in portrait orientation normalized by focal length
+  optional float portrait_height = 3 [default = 1.3435];
+  enum ImageOrientation {
+    PORTRAIT_ORIENTATION = 0;
+    LANDSCAPE_ORIENTATION = 1;
+  }
+  // The input image orientation
+  optional ImageOrientation image_orientation = 4
+      [default = PORTRAIT_ORIENTATION];
+
+  // This defines the projection method from 2D screen to 3D.
+  enum ProjectionMode {
+    UNSPECIFIED = 0;
+    // Projects 2D point to ground plane (horizontal plane).
+    GROUND_PLANE = 1;
+    // Projects 2D point to sphere.
+    SPHERE = 2;
+  }
+  optional ProjectionMode projection_mode = 5 [default = GROUND_PLANE];
+  // Radius of sphere when using the SPHERE projection mode above.
+  // The value is normalized by focal length.
+  optional float projection_sphere_radius = 6 [default = 100.0];
+}
diff --git a/mediapipe/modules/objectron/calculators/decoder.cc b/mediapipe/modules/objectron/calculators/decoder.cc
new file mode 100644
index 0000000..0af3458
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/decoder.cc
@@ -0,0 +1,252 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/decoder.h"
+
+#include <limits>
+#include <vector>
+
+#include "Eigen/Core"
+#include "Eigen/Dense"
+#include "absl/status/status.h"
+#include "mediapipe/framework/port/canonical_errors.h"
+#include "mediapipe/framework/port/logging.h"
+#include "mediapipe/framework/port/opencv_imgproc_inc.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/box.h"
+#include "mediapipe/modules/objectron/calculators/epnp.h"
+#include "mediapipe/modules/objectron/calculators/types.h"
+
+namespace mediapipe {
+
+constexpr int Decoder::kNumOffsetmaps = 16;
+constexpr int kNumKeypoints = 9;
+
+namespace {
+
+inline void SetPoint3d(const Eigen::Vector3f& point_vec, Point3D* point_3d) {
+  point_3d->set_x(point_vec.x());
+  point_3d->set_y(point_vec.y());
+  point_3d->set_z(point_vec.z());
+}
+
+}  // namespace
+
+FrameAnnotation Decoder::DecodeBoundingBoxKeypoints(
+    const cv::Mat& heatmap, const cv::Mat& offsetmap) const {
+  CHECK_EQ(1, heatmap.channels());
+  CHECK_EQ(kNumOffsetmaps, offsetmap.channels());
+  CHECK_EQ(heatmap.cols, offsetmap.cols);
+  CHECK_EQ(heatmap.rows, offsetmap.rows);
+
+  const float offset_scale = std::min(offsetmap.cols, offsetmap.rows);
+  const std::vector<cv::Point> center_points = ExtractCenterKeypoints(heatmap);
+  std::vector<BeliefBox> boxes;
+  for (const auto& center_point : center_points) {
+    BeliefBox box;
+    box.box_2d.emplace_back(center_point.x, center_point.y);
+    const int center_x = static_cast<int>(std::round(center_point.x));
+    const int center_y = static_cast<int>(std::round(center_point.y));
+    box.belief = heatmap.at<float>(center_y, center_x);
+    if (config_.voting_radius() > 1) {
+      DecodeByVoting(heatmap, offsetmap, center_x, center_y, offset_scale,
+                     offset_scale, &box);
+    } else {
+      DecodeByPeak(offsetmap, center_x, center_y, offset_scale, offset_scale,
+                   &box);
+    }
+    if (IsNewBox(&boxes, &box)) {
+      boxes.push_back(std::move(box));
+    }
+  }
+
+  const float x_scale = 1.0f / offsetmap.cols;
+  const float y_scale = 1.0f / offsetmap.rows;
+  FrameAnnotation frame_annotations;
+  for (const auto& box : boxes) {
+    auto* object = frame_annotations.add_annotations();
+    for (const auto& point : box.box_2d) {
+      auto* point2d = object->add_keypoints()->mutable_point_2d();
+      point2d->set_x(point.first * x_scale);
+      point2d->set_y(point.second * y_scale);
+    }
+  }
+  return frame_annotations;
+}
+
+void Decoder::DecodeByPeak(const cv::Mat& offsetmap, int center_x, int center_y,
+                           float offset_scale_x, float offset_scale_y,
+                           BeliefBox* box) const {
+  const auto& offset = offsetmap.at<cv::Vec<float, kNumOffsetmaps>>(
+      /*row*/ center_y, /*col*/ center_x);
+  for (int i = 0; i < kNumOffsetmaps / 2; ++i) {
+    const float x_offset = offset[2 * i] * offset_scale_x;
+    const float y_offset = offset[2 * i + 1] * offset_scale_y;
+    box->box_2d.emplace_back(center_x + x_offset, center_y + y_offset);
+  }
+}
+
+void Decoder::DecodeByVoting(const cv::Mat& heatmap, const cv::Mat& offsetmap,
+                             int center_x, int center_y, float offset_scale_x,
+                             float offset_scale_y, BeliefBox* box) const {
+  // Votes at the center.
+  const auto& center_offset = offsetmap.at<cv::Vec<float, kNumOffsetmaps>>(
+      /*row*/ center_y, /*col*/ center_x);
+  std::vector<float> center_votes(kNumOffsetmaps, 0.f);
+  for (int i = 0; i < kNumOffsetmaps / 2; ++i) {
+    center_votes[2 * i] = center_x + center_offset[2 * i] * offset_scale_x;
+    center_votes[2 * i + 1] =
+        center_y + center_offset[2 * i + 1] * offset_scale_y;
+  }
+
+  // Find voting window.
+  int x_min = std::max(0, center_x - config_.voting_radius());
+  int y_min = std::max(0, center_y - config_.voting_radius());
+  int width = std::min(heatmap.cols - x_min, config_.voting_radius() * 2 + 1);
+  int height = std::min(heatmap.rows - y_min, config_.voting_radius() * 2 + 1);
+  cv::Rect rect(x_min, y_min, width, height);
+  cv::Mat heat = heatmap(rect);
+  cv::Mat offset = offsetmap(rect);
+
+  for (int i = 0; i < kNumOffsetmaps / 2; ++i) {
+    float x_sum = 0.f;
+    float y_sum = 0.f;
+    float votes = 0.f;
+    for (int r = 0; r < heat.rows; ++r) {
+      for (int c = 0; c < heat.cols; ++c) {
+        const float belief = heat.at<float>(r, c);
+        if (belief < config_.voting_threshold()) {
+          continue;
+        }
+        float offset_x =
+            offset.at<cv::Vec<float, kNumOffsetmaps>>(r, c)[2 * i] *
+            offset_scale_x;
+        float offset_y =
+            offset.at<cv::Vec<float, kNumOffsetmaps>>(r, c)[2 * i + 1] *
+            offset_scale_y;
+        float vote_x = c + rect.x + offset_x;
+        float vote_y = r + rect.y + offset_y;
+        float x_diff = std::abs(vote_x - center_votes[2 * i]);
+        float y_diff = std::abs(vote_y - center_votes[2 * i + 1]);
+        if (x_diff > config_.voting_allowance() ||
+            y_diff > config_.voting_allowance()) {
+          continue;
+        }
+        x_sum += vote_x * belief;
+        y_sum += vote_y * belief;
+        votes += belief;
+      }
+    }
+    box->box_2d.emplace_back(x_sum / votes, y_sum / votes);
+  }
+}
+
+bool Decoder::IsNewBox(std::vector<BeliefBox>* boxes, BeliefBox* box) const {
+  for (auto& b : *boxes) {
+    if (IsIdentical(b, *box)) {
+      if (b.belief < box->belief) {
+        std::swap(b, *box);
+      }
+      return false;
+    }
+  }
+  return true;
+}
+
+bool Decoder::IsIdentical(const BeliefBox& box_1,
+                          const BeliefBox& box_2) const {
+  // Skip the center point.
+  for (int i = 1; i < box_1.box_2d.size(); ++i) {
+    const float x_diff =
+        std::abs(box_1.box_2d[i].first - box_2.box_2d[i].first);
+    const float y_diff =
+        std::abs(box_1.box_2d[i].second - box_2.box_2d[i].second);
+    if (x_diff > config_.voting_allowance() ||
+        y_diff > config_.voting_allowance()) {
+      return false;
+    }
+  }
+  return true;
+}
+
+std::vector<cv::Point> Decoder::ExtractCenterKeypoints(
+    const cv::Mat& center_heatmap) const {
+  cv::Mat max_filtered_heatmap(center_heatmap.rows, center_heatmap.cols,
+                               center_heatmap.type());
+  const int kernel_size =
+      static_cast<int>(config_.local_max_distance() * 2 + 1 + 0.5f);
+  const cv::Size morph_size(kernel_size, kernel_size);
+  cv::dilate(center_heatmap, max_filtered_heatmap,
+             cv::getStructuringElement(cv::MORPH_RECT, morph_size));
+  cv::Mat peak_map;
+  cv::bitwise_and((center_heatmap >= max_filtered_heatmap),
+                  (center_heatmap >= config_.heatmap_threshold()), peak_map);
+  std::vector<cv::Point> locations;  // output, locations of non-zero pixels
+  cv::findNonZero(peak_map, locations);
+  return locations;
+}
+
+absl::Status Decoder::Lift2DTo3D(
+    const Eigen::Matrix<float, 4, 4, Eigen::RowMajor>& projection_matrix,
+    bool portrait, FrameAnnotation* estimated_box) const {
+  CHECK(estimated_box != nullptr);
+
+  for (auto& annotation : *estimated_box->mutable_annotations()) {
+    CHECK_EQ(kNumKeypoints, annotation.keypoints_size());
+
+    // Fill input 2D Points;
+    std::vector<Vector2f> input_points_2d;
+    input_points_2d.reserve(kNumKeypoints);
+    for (const auto& keypoint : annotation.keypoints()) {
+      input_points_2d.emplace_back(keypoint.point_2d().x(),
+                                   keypoint.point_2d().y());
+    }
+
+    // Run EPnP.
+    std::vector<Vector3f> output_points_3d;
+    output_points_3d.reserve(kNumKeypoints);
+    auto status = SolveEpnp(projection_matrix, portrait, input_points_2d,
+                            &output_points_3d);
+    if (!status.ok()) {
+      LOG(ERROR) << status;
+      return status;
+    }
+
+    // Fill 3D keypoints;
+    for (int i = 0; i < kNumKeypoints; ++i) {
+      SetPoint3d(output_points_3d[i],
+                 annotation.mutable_keypoints(i)->mutable_point_3d());
+    }
+
+    // Fit a box to the 3D points to get box scale, rotation, translation.
+    Box box("category");
+    box.Fit(output_points_3d);
+    const Eigen::Matrix<float, 3, 3, Eigen::RowMajor> rotation =
+        box.GetRotation();
+    const Eigen::Vector3f translation = box.GetTranslation();
+    const Eigen::Vector3f scale = box.GetScale();
+    // Fill box rotation.
+    *annotation.mutable_rotation() = {rotation.data(),
+                                      rotation.data() + rotation.size()};
+    // Fill box translation.
+    *annotation.mutable_translation() = {
+        translation.data(), translation.data() + translation.size()};
+    // Fill box scale.
+    *annotation.mutable_scale() = {scale.data(), scale.data() + scale.size()};
+  }
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/decoder.h b/mediapipe/modules/objectron/calculators/decoder.h
new file mode 100644
index 0000000..be69939
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/decoder.h
@@ -0,0 +1,109 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_DECODER_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_DECODER_H_
+
+#include <vector>
+
+#include "Eigen/Dense"
+#include "absl/status/status.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/belief_decoder_config.pb.h"
+
+namespace mediapipe {
+
+// Decodes 3D bounding box from heatmaps and offset maps. In the future,
+// if we want to develop decoder for generic skeleton, then we need to
+// generalize this class, and make a few child classes.
+class Decoder {
+ public:
+  static const int kNumOffsetmaps;
+
+  explicit Decoder(const BeliefDecoderConfig& config) : config_(config) {
+    epnp_alpha_ << 4.0f, -1.0f, -1.0f, -1.0f, 2.0f, -1.0f, -1.0f, 1.0f, 2.0f,
+        -1.0f, 1.0f, -1.0f, 0.0f, -1.0f, 1.0f, 1.0f, 2.0f, 1.0f, -1.0f, -1.0f,
+        0.0f, 1.0f, -1.0f, 1.0f, 0.0f, 1.0f, 1.0f, -1.0f, -2.0f, 1.0f, 1.0f,
+        1.0f;
+  }
+
+  // Decodes bounding boxes from predicted heatmap and offset maps.
+  // Input:
+  //   heatmap: a single channel cv::Mat representing center point heatmap
+  //   offsetmap: a 16 channel cv::Mat representing the 16 offset maps
+  //              (2 for each of the 8 vertices)
+  // Output:
+  //   Outputs 3D bounding boxes 2D vertices, represented by 'point_2d' field
+  //   in each 'keypoints' field of object annotations.
+  FrameAnnotation DecodeBoundingBoxKeypoints(const cv::Mat& heatmap,
+                                             const cv::Mat& offsetmap) const;
+
+  // Lifts the estimated 2D projections of bounding box vertices to 3D.
+  // This function uses the EPnP approach described in this paper:
+  // https://icwww.epfl.ch/~lepetit/papers/lepetit_ijcv08.pdf .
+  // Input:
+  //   projection_matrix: the projection matrix from 3D coordinate
+  //     to screen coordinate.
+  //     The 2D screen coordinate is defined as: u is along the long
+  //     edge of the device, pointing down; v is along the short edge
+  //     of the device, pointing right.
+  //   portrait: a boolen variable indicating whether our images are
+  //     obtained in portrait orientation or not.
+  //   estimated_box: annotation with point_2d field populated with
+  //     2d vertices.
+  // Output:
+  //   estimated_box: annotation with point_3d field populated with
+  //     3d vertices.
+  absl::Status Lift2DTo3D(
+      const Eigen::Matrix<float, 4, 4, Eigen::RowMajor>& projection_matrix,
+      bool portrait, FrameAnnotation* estimated_box) const;
+
+ private:
+  struct BeliefBox {
+    float belief;
+    std::vector<std::pair<float, float>> box_2d;
+  };
+
+  std::vector<cv::Point> ExtractCenterKeypoints(
+      const cv::Mat& center_heatmap) const;
+
+  // Decodes 2D keypoints at the peak point.
+  void DecodeByPeak(const cv::Mat& offsetmap, int center_x, int center_y,
+                    float offset_scale_x, float offset_scale_y,
+                    BeliefBox* box) const;
+
+  // Decodes 2D keypoints by voting around the peak.
+  void DecodeByVoting(const cv::Mat& heatmap, const cv::Mat& offsetmap,
+                      int center_x, int center_y, float offset_scale_x,
+                      float offset_scale_y, BeliefBox* box) const;
+
+  // Returns true if it is a new box. Otherwise, it may replace an existing box
+  // if the new box's belief is higher.
+  bool IsNewBox(std::vector<BeliefBox>* boxes, BeliefBox* box) const;
+
+  // Returns true if the two boxes are identical.
+  bool IsIdentical(const BeliefBox& box_1, const BeliefBox& box_2) const;
+
+  BeliefDecoderConfig config_;
+  // Following equation (1) in this paper
+  // https://icwww.epfl.ch/~lepetit/papers/lepetit_ijcv08.pdf,
+  // this variable denotes the coefficients for the 4 control points
+  // for each of the 8 3D box vertices.
+  Eigen::Matrix<float, 8, 4, Eigen::RowMajor> epnp_alpha_;
+};
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_DECODER_H_
diff --git a/mediapipe/modules/objectron/calculators/epnp.cc b/mediapipe/modules/objectron/calculators/epnp.cc
new file mode 100644
index 0000000..8bd7151
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/epnp.cc
@@ -0,0 +1,167 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/epnp.h"
+
+namespace mediapipe {
+
+namespace {
+
+// NUmber of keypoints.
+constexpr int kNumKeypoints = 9;
+
+using Eigen::Map;
+using Eigen::Matrix;
+using Eigen::Matrix4f;
+using Eigen::Vector2f;
+using Eigen::Vector3f;
+
+}  // namespace
+
+absl::Status SolveEpnp(const float focal_x, const float focal_y,
+                       const float center_x, const float center_y,
+                       const bool portrait,
+                       const std::vector<Vector2f>& input_points_2d,
+                       std::vector<Vector3f>* output_points_3d) {
+  if (input_points_2d.size() != kNumKeypoints) {
+    return absl::InvalidArgumentError(
+        absl::StrFormat("Input must has %d 2D points.", kNumKeypoints));
+  }
+
+  if (output_points_3d == nullptr) {
+    return absl::InvalidArgumentError(
+        "Output pointer output_points_3d is Null.");
+  }
+
+  Matrix<float, (kNumKeypoints - 1) * 2, 12> m =
+      Matrix<float, (kNumKeypoints - 1) * 2, 12>::Zero();
+
+  Matrix<float, kNumKeypoints - 1, 4> epnp_alpha;
+  // The epnp_alpha is the Nx4 weight matrix from the EPnP paper, which is used
+  // to express the N box vertices as the weighted sum of 4 control points. The
+  // value of epnp_alpha is depedent on the set of control points been used.
+  // In our case we used the 4 control points as below (coordinates are in world
+  // coordinate system):
+  //     c0 = (0.0, 0.0, 0.0)  // Box center
+  //     c1 = (1.0, 0.0, 0.0)  // Right face center
+  //     c2 = (0.0, 1.0, 0.0)  // Top face center
+  //     c3 = (0.0, 0.0, 1.0)  // Front face center
+  //
+  //       3 + + + + + + + + 7
+  //       +\                +\          UP
+  //       + \               + \
+  //       +  \              +  \        |
+  //       +   4 + + + + + + + + 8       | y
+  //       +   +             +   +       |
+  //       +   +             +   +       |
+  //       +   +     (0)     +   +       .------- x
+  //       +   +             +   +        \
+  //       1 + + + + + + + + 5   +         \
+  //        \  +              \  +          \ z
+  //         \ +               \ +           \
+  //          \+                \+
+  //           2 + + + + + + + + 6
+  //
+  // For each box vertex shown above, we have the below weighted sum expression:
+  //   v1 = c0 - (c1 - c0) - (c2 - c0) - (c3 - c0) = 4*c0 - c1 - c2 - c3;
+  //   v2 = c0 - (c1 - c0) - (c2 - c0) + (c3 - c0) = 2*c0 - c1 - c2 + c3;
+  //   v3 = c0 - (c1 - c0) + (c2 - c0) - (c3 - c0) = 2*c0 - c1 + c2 - c3;
+  //   ...
+  // Thus we can determine the value of epnp_alpha as been used below.
+  //
+  // clang-format off
+  epnp_alpha << 4.0f, -1.0f, -1.0f, -1.0f,
+                2.0f, -1.0f, -1.0f,  1.0f,
+                2.0f, -1.0f,  1.0f, -1.0f,
+                0.0f, -1.0f,  1.0f,  1.0f,
+                2.0f,  1.0f, -1.0f, -1.0f,
+                0.0f,  1.0f, -1.0f,  1.0f,
+                0.0f,  1.0f,  1.0f, -1.0f,
+               -2.0f,  1.0f,  1.0f,  1.0f;
+  // clang-format on
+
+  for (int i = 0; i < input_points_2d.size() - 1; ++i) {
+    // Skip 0th landmark which is object center.
+    const auto& point_2d = input_points_2d[i + 1];
+
+    // Convert 2d point from `pixel coordinates` to `NDC coordinates`([-1, 1])
+    // following to the definitions in:
+    // https://google.github.io/mediapipe/solutions/objectron#ndc-space
+    // If portrait mode is been used, it's the caller's responsibility to
+    // convert the input 2d points' coordinates.
+    float x_ndc, y_ndc;
+    if (portrait) {
+      x_ndc = point_2d.y() * 2 - 1;
+      y_ndc = point_2d.x() * 2 - 1;
+    } else {
+      x_ndc = point_2d.x() * 2 - 1;
+      y_ndc = 1 - point_2d.y() * 2;
+    }
+
+    for (int j = 0; j < 4; ++j) {
+      // For each of the 4 control points, formulate two rows of the
+      // m matrix (two equations).
+      const float control_alpha = epnp_alpha(i, j);
+      m(i * 2, j * 3) = focal_x * control_alpha;
+      m(i * 2, j * 3 + 2) = (center_x + x_ndc) * control_alpha;
+      m(i * 2 + 1, j * 3 + 1) = focal_y * control_alpha;
+      m(i * 2 + 1, j * 3 + 2) = (center_y + y_ndc) * control_alpha;
+    }
+  }
+  // This is a self adjoint matrix. Use SelfAdjointEigenSolver for a fast
+  // and stable solution.
+  Matrix<float, 12, 12> mt_m = m.transpose() * m;
+  Eigen::SelfAdjointEigenSolver<Matrix<float, 12, 12>> eigen_solver(mt_m);
+  if (eigen_solver.info() != Eigen::Success) {
+    return absl::AbortedError("Eigen decomposition failed.");
+  }
+  CHECK_EQ(12, eigen_solver.eigenvalues().size());
+
+  // Eigenvalues are sorted in increasing order for SelfAdjointEigenSolver
+  // only! If you use other Eigen Solvers, it's not guaranteed to be in
+  // increasing order. Here, we just take the eigen vector corresponding
+  // to first/smallest eigen value, since we used SelfAdjointEigenSolver.
+  Eigen::VectorXf eigen_vec = eigen_solver.eigenvectors().col(0);
+  Map<Matrix<float, 4, 3, Eigen::RowMajor>> control_matrix(eigen_vec.data());
+
+  // All 3D points should be in front of camera (z < 0).
+  if (control_matrix(0, 2) > 0) {
+    control_matrix = -control_matrix;
+  }
+  Matrix<float, kNumKeypoints - 1, 3> vertices = epnp_alpha * control_matrix;
+
+  // Fill 0th 3D points.
+  output_points_3d->emplace_back(control_matrix(0, 0), control_matrix(0, 1),
+                                 control_matrix(0, 2));
+  // Fill the rest 3D points.
+  for (int i = 0; i < kNumKeypoints - 1; ++i) {
+    output_points_3d->emplace_back(vertices(i, 0), vertices(i, 1),
+                                   vertices(i, 2));
+  }
+  return absl::OkStatus();
+}
+
+absl::Status SolveEpnp(const Eigen::Matrix4f& projection_matrix,
+                       const bool portrait,
+                       const std::vector<Vector2f>& input_points_2d,
+                       std::vector<Vector3f>* output_points_3d) {
+  const float focal_x = projection_matrix(0, 0);
+  const float focal_y = projection_matrix(1, 1);
+  const float center_x = projection_matrix(0, 2);
+  const float center_y = projection_matrix(1, 2);
+  return SolveEpnp(focal_x, focal_y, center_x, center_y, portrait,
+                   input_points_2d, output_points_3d);
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/epnp.h b/mediapipe/modules/objectron/calculators/epnp.h
new file mode 100644
index 0000000..85be6f9
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/epnp.h
@@ -0,0 +1,62 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_EPNP_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_EPNP_H_
+
+#include <vector>
+
+#include "Eigen/Dense"
+#include "absl/status/status.h"
+#include "absl/strings/str_format.h"
+#include "mediapipe/framework/port/logging.h"
+
+namespace mediapipe {
+
+// This function performs EPnP algorithm, lifting normalized 2D points in pixel
+// space to 3D points in camera coordinate.
+//
+// Inputs:
+//   focal_x: camera focal length along x.
+//   focal_y: camera focal length along y.
+//   center_x: camera center along x.
+//   center_y: camera center along y.
+//   portrait: a boolen variable indicating whether our images are obtained in
+//     portrait orientation or not.
+//   input_points_2d: input 2D points to be lifted to 3D.
+//   output_points_3d: ouput 3D points in camera coordinate.
+absl::Status SolveEpnp(const float focal_x, const float focal_y,
+                       const float center_x, const float center_y,
+                       const bool portrait,
+                       const std::vector<Eigen::Vector2f>& input_points_2d,
+                       std::vector<Eigen::Vector3f>* output_points_3d);
+
+// This function performs EPnP algorithm, lifting normalized 2D points in pixel
+// space to 3D points in camera coordinate.
+//
+// Inputs:
+//   projection_matrix: the projection matrix from 3D coordinate
+//     to screen coordinate.
+//   portrait: a boolen variable indicating whether our images are obtained in
+//     portrait orientation or not.
+//   input_points_2d: input 2D points to be lifted to 3D.
+//   output_points_3d: ouput 3D points in camera coordinate.
+absl::Status SolveEpnp(const Eigen::Matrix4f& projection_matrix,
+                       const bool portrait,
+                       const std::vector<Eigen::Vector2f>& input_points_2d,
+                       std::vector<Eigen::Vector3f>* output_points_3d);
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_EPNP_H_
diff --git a/mediapipe/modules/objectron/calculators/epnp_test.cc b/mediapipe/modules/objectron/calculators/epnp_test.cc
new file mode 100644
index 0000000..8cf218a
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/epnp_test.cc
@@ -0,0 +1,169 @@
+#include "mediapipe/modules/objectron/calculators/epnp.h"
+
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/tool/test_util.h"
+
+namespace mediapipe {
+namespace {
+
+using Eigen::AngleAxisf;
+using Eigen::Map;
+using Eigen::Matrix;
+using Eigen::Matrix4f;
+using Eigen::RowMajor;
+using Eigen::Vector2f;
+using Eigen::Vector3f;
+using ::testing::HasSubstr;
+using ::testing::Test;
+using ::testing::status::StatusIs;
+using Matrix3f = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
+
+constexpr uint8_t kNumKeypoints = 9;
+
+// clang-format off
+constexpr float kUnitBox[] = { 0.0f,  0.0f,  0.0f,
+                              -0.5f, -0.5f, -0.5f,
+                              -0.5f, -0.5f,  0.5f,
+                              -0.5f,  0.5f, -0.5f,
+                              -0.5f,  0.5f,  0.5f,
+                               0.5f, -0.5f, -0.5f,
+                               0.5f, -0.5f,  0.5f,
+                               0.5f,  0.5f, -0.5f,
+                               0.5f,  0.5f,  0.5f, };
+// clang-format on
+
+constexpr float kFocalX = 1.0f;
+constexpr float kFocalY = 1.0f;
+constexpr float kCenterX = 0.0f;
+constexpr float kCenterY = 0.0f;
+
+constexpr float kAzimuth = 90.0f * M_PI / 180.0f;
+constexpr float kElevation = 45.0f * M_PI / 180.0f;
+constexpr float kTilt = 15.0f * M_PI / 180.0f;
+
+constexpr float kTranslationArray[] = {0.0f, 0.0f, -100.0f};
+
+constexpr float kScaleArray[] = {50.0f, 50.0f, 50.0f};
+
+class SolveEpnpTest : public Test {
+ protected:
+  SolveEpnpTest() {}
+
+  void SetUp() override {
+    // Create vertices in world frame.
+    Map<const Matrix<float, kNumKeypoints, 3, RowMajor>> vertices_w(kUnitBox);
+
+    // Create Pose.
+    Matrix3f rotation;
+    rotation = AngleAxisf(kTilt, Vector3f::UnitZ()) *
+               AngleAxisf(kElevation, Vector3f::UnitX()) *
+               AngleAxisf(kAzimuth, Vector3f::UnitY());
+    Map<const Vector3f> translation(kTranslationArray);
+    Map<const Vector3f> scale(kScaleArray);
+
+    // Generate 3d vertices in camera frame.
+    const auto vertices_c =
+        ((rotation * scale.asDiagonal() * vertices_w.transpose()).colwise() +
+         translation)
+            .transpose();
+
+    // Generate input 2d points.
+    std::vector<Vector2f> input_2d_points;
+    std::vector<Vector3f> expected_3d_points;
+    for (int i = 0; i < kNumKeypoints; ++i) {
+      const auto x = vertices_c(i, 0);
+      const auto y = vertices_c(i, 1);
+      const auto z = vertices_c(i, 2);
+
+      const float x_ndc = -kFocalX * x / z + kCenterX;
+      const float y_ndc = -kFocalY * y / z + kCenterY;
+
+      const float x_pixel = (1.0f + x_ndc) / 2.0f;
+      const float y_pixel = (1.0f - y_ndc) / 2.0f;
+
+      expected_3d_points_.emplace_back(x, y, z);
+      input_2d_points_.emplace_back(x_pixel, y_pixel);
+    }
+  }
+
+  void VerifyOutput3dPoints(const std::vector<Vector3f>& output_3d_points) {
+    EXPECT_EQ(kNumKeypoints, output_3d_points.size());
+    const float scale = output_3d_points[0].z() / expected_3d_points_[0].z();
+    for (int i = 0; i < kNumKeypoints; ++i) {
+      EXPECT_NEAR(output_3d_points[i].x(), expected_3d_points_[i].x() * scale,
+                  2.e-6f);
+      EXPECT_NEAR(output_3d_points[i].y(), expected_3d_points_[i].y() * scale,
+                  2.e-6f);
+      EXPECT_NEAR(output_3d_points[i].z(), expected_3d_points_[i].z() * scale,
+                  2.e-6f);
+    }
+  }
+
+  std::vector<Vector2f> input_2d_points_;
+  std::vector<Vector3f> expected_3d_points_;
+};
+
+TEST_F(SolveEpnpTest, SolveEpnp) {
+  std::vector<Vector3f> output_3d_points;
+  MP_ASSERT_OK(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
+                         /*portrait*/ false, input_2d_points_,
+                         &output_3d_points));
+  // Test output 3D points.
+  VerifyOutput3dPoints(output_3d_points);
+}
+
+TEST_F(SolveEpnpTest, SolveEpnppPortrait) {
+  std::vector<Vector3f> output_3d_points;
+  MP_ASSERT_OK(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
+                         /*portrait*/ true, input_2d_points_,
+                         &output_3d_points));
+  // Test output 3D points.
+  for (auto& point_3d : output_3d_points) {
+    const auto x = point_3d.x();
+    const auto y = point_3d.y();
+    // Convert from portrait mode to normal mode, y => x, x => -y.
+    point_3d.x() = y;
+    point_3d.y() = -x;
+  }
+  VerifyOutput3dPoints(output_3d_points);
+}
+
+TEST_F(SolveEpnpTest, SolveEpnpProjectionMatrix) {
+  Matrix4f projection_matrix;
+  // clang-format off
+  projection_matrix << kFocalX,    0.0f, kCenterX, 0.0f,
+                          0.0f, kFocalY, kCenterY, 0.0f,
+                          0.0f,    0.0f,    -1.0f, 0.0f,
+                          0.0f,    0.0f,    -1.0f, 0.0f;
+  // clang-format on
+
+  std::vector<Vector3f> output_3d_points;
+  MP_ASSERT_OK(SolveEpnp(projection_matrix, /*portrait*/ false,
+                         input_2d_points_, &output_3d_points));
+
+  // Test output 3D points.
+  VerifyOutput3dPoints(output_3d_points);
+}
+
+TEST_F(SolveEpnpTest, BadInput2dPoints) {
+  // Generate empty input 2D points.
+  std::vector<Vector2f> input_2d_points;
+  std::vector<Vector3f> output_3d_points;
+  EXPECT_THAT(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
+                        /*portrait*/ false, input_2d_points, &output_3d_points),
+              StatusIs(absl::StatusCode::kInvalidArgument,
+                       HasSubstr("Input must has")));
+}
+
+TEST_F(SolveEpnpTest, BadOutput3dPoints) {
+  // Generate null output 3D points.
+  std::vector<Vector3f>* output_3d_points = nullptr;
+  EXPECT_THAT(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
+                        /*portrait*/ false, input_2d_points_, output_3d_points),
+              StatusIs(absl::StatusCode::kInvalidArgument,
+                       "Output pointer output_points_3d is Null."));
+}
+
+}  // namespace
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/filter_detection_calculator.cc b/mediapipe/modules/objectron/calculators/filter_detection_calculator.cc
new file mode 100644
index 0000000..0f29f9c
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/filter_detection_calculator.cc
@@ -0,0 +1,262 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "absl/container/node_hash_set.h"
+#include "absl/strings/str_split.h"
+#include "absl/strings/string_view.h"
+#include "absl/strings/strip.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/detection.pb.h"
+#include "mediapipe/framework/formats/location_data.pb.h"
+#include "mediapipe/framework/port/logging.h"
+#include "mediapipe/framework/port/map_util.h"
+#include "mediapipe/framework/port/re2.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/modules/objectron/calculators/filter_detection_calculator.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kDetectionTag[] = "DETECTION";
+constexpr char kDetectionsTag[] = "DETECTIONS";
+constexpr char kLabelsTag[] = "LABELS";
+constexpr char kLabelsCsvTag[] = "LABELS_CSV";
+
+using mediapipe::RE2;
+using Detections = std::vector<Detection>;
+using Strings = std::vector<std::string>;
+
+}  // namespace
+
+// Filters the entries in a Detection to only those with valid scores
+// for the specified allowed labels. Allowed labels are provided as a
+// vector<std::string> in an optional input side packet. Allowed labels can
+// contain simple strings or regular expressions. The valid score range
+// can be set in the options.The allowed labels can be provided as
+// vector<std::string> (LABELS) or CSV std::string (LABELS_CSV) containing class
+// names of allowed labels. Note: Providing an empty vector in the input side
+// packet Packet causes this calculator to act as a sink if
+// empty_allowed_labels_means_allow_everything is set to false (default value).
+// To allow all labels, use the calculator with no input side packet stream, or
+// set empty_allowed_labels_means_allow_everything to true.
+//
+// Example config:
+// node {
+//   calculator: "FilterDetectionCalculator"
+//   input_stream: "DETECTIONS:detections"
+//   output_stream: "DETECTIONS:filtered_detections"
+//   input_side_packet: "LABELS:allowed_labels"
+//   options: {
+//     [mediapipe.FilterDetectionCalculatorOptions.ext]: {
+//       min_score: 0.5
+//     }
+//   }
+// }
+
+struct FirstGreaterComparator {
+  bool operator()(const std::pair<float, int>& a,
+                  const std::pair<float, int>& b) const {
+    return a.first > b.first;
+  }
+};
+
+absl::Status SortLabelsByDecreasingScore(const Detection& detection,
+                                         Detection* sorted_detection) {
+  RET_CHECK(sorted_detection);
+  RET_CHECK_EQ(detection.score_size(), detection.label_size());
+  if (!detection.label_id().empty()) {
+    RET_CHECK_EQ(detection.score_size(), detection.label_id_size());
+  }
+  // Copies input to keep all fields unchanged, and to reserve space for
+  // repeated fields. Repeated fields (score, label, and label_id) will be
+  // overwritten.
+  *sorted_detection = detection;
+
+  std::vector<std::pair<float, int>> scores_and_indices(detection.score_size());
+  for (int i = 0; i < detection.score_size(); ++i) {
+    scores_and_indices[i].first = detection.score(i);
+    scores_and_indices[i].second = i;
+  }
+
+  std::sort(scores_and_indices.begin(), scores_and_indices.end(),
+            FirstGreaterComparator());
+
+  for (int i = 0; i < detection.score_size(); ++i) {
+    const int index = scores_and_indices[i].second;
+    sorted_detection->set_score(i, detection.score(index));
+    sorted_detection->set_label(i, detection.label(index));
+  }
+
+  if (!detection.label_id().empty()) {
+    for (int i = 0; i < detection.score_size(); ++i) {
+      const int index = scores_and_indices[i].second;
+      sorted_detection->set_label_id(i, detection.label_id(index));
+    }
+  }
+  return absl::OkStatus();
+}
+
+class FilterDetectionCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  bool IsValidLabel(const std::string& label);
+  bool IsValidScore(float score);
+  // Stores numeric limits for filtering on the score.
+  FilterDetectionCalculatorOptions options_;
+  // We use the next two fields to possibly filter to a limited set of
+  // classes.  The hash_set will be empty in two cases: 1) if no input
+  // side packet stream is provided (not filtering on labels), or 2)
+  // if the input side packet contains an empty vector (no labels are
+  // allowed). We use limit_labels_ to distinguish between the two cases.
+  bool limit_labels_ = true;
+  absl::node_hash_set<std::string> allowed_labels_;
+};
+REGISTER_CALCULATOR(FilterDetectionCalculator);
+
+absl::Status FilterDetectionCalculator::GetContract(CalculatorContract* cc) {
+  RET_CHECK(!cc->Inputs().GetTags().empty());
+  RET_CHECK(!cc->Outputs().GetTags().empty());
+
+  if (cc->Inputs().HasTag(kDetectionTag)) {
+    cc->Inputs().Tag(kDetectionTag).Set<Detection>();
+    cc->Outputs().Tag(kDetectionTag).Set<Detection>();
+  }
+  if (cc->Inputs().HasTag(kDetectionsTag)) {
+    cc->Inputs().Tag(kDetectionsTag).Set<Detections>();
+    cc->Outputs().Tag(kDetectionsTag).Set<Detections>();
+  }
+  if (cc->InputSidePackets().HasTag(kLabelsTag)) {
+    cc->InputSidePackets().Tag(kLabelsTag).Set<Strings>();
+  }
+  if (cc->InputSidePackets().HasTag(kLabelsCsvTag)) {
+    cc->InputSidePackets().Tag(kLabelsCsvTag).Set<std::string>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status FilterDetectionCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  options_ = cc->Options<FilterDetectionCalculatorOptions>();
+  limit_labels_ = cc->InputSidePackets().HasTag(kLabelsTag) ||
+                  cc->InputSidePackets().HasTag(kLabelsCsvTag);
+  if (limit_labels_) {
+    Strings allowlist_labels;
+    if (cc->InputSidePackets().HasTag(kLabelsCsvTag)) {
+      allowlist_labels = absl::StrSplit(
+          cc->InputSidePackets().Tag(kLabelsCsvTag).Get<std::string>(), ',',
+          absl::SkipWhitespace());
+      for (auto& e : allowlist_labels) {
+        absl::StripAsciiWhitespace(&e);
+      }
+    } else {
+      allowlist_labels = cc->InputSidePackets().Tag(kLabelsTag).Get<Strings>();
+    }
+    allowed_labels_.insert(allowlist_labels.begin(), allowlist_labels.end());
+  }
+  if (limit_labels_ && allowed_labels_.empty()) {
+    if (options_.fail_on_empty_labels()) {
+      cc->GetCounter("VideosWithEmptyLabelsAllowlist")->Increment();
+      return tool::StatusFail(
+          "FilterDetectionCalculator received empty allowlist with "
+          "fail_on_empty_labels = true.");
+    }
+    if (options_.empty_allowed_labels_means_allow_everything()) {
+      // Continue as if side_input was not provided, i.e. pass all labels.
+      limit_labels_ = false;
+    }
+  }
+  return absl::OkStatus();
+}
+
+absl::Status FilterDetectionCalculator::Process(CalculatorContext* cc) {
+  if (limit_labels_ && allowed_labels_.empty()) {
+    return absl::OkStatus();
+  }
+  Detections detections;
+  if (cc->Inputs().HasTag(kDetectionsTag)) {
+    detections = cc->Inputs().Tag(kDetectionsTag).Get<Detections>();
+  } else if (cc->Inputs().HasTag(kDetectionTag)) {
+    detections.emplace_back(cc->Inputs().Tag(kDetectionsTag).Get<Detection>());
+  }
+  std::unique_ptr<Detections> outputs(new Detections);
+  for (const auto& input : detections) {
+    Detection output;
+    for (int i = 0; i < input.label_size(); ++i) {
+      const std::string& label = input.label(i);
+      const float score = input.score(i);
+      if (IsValidLabel(label) && IsValidScore(score)) {
+        output.add_label(label);
+        output.add_score(score);
+      }
+    }
+    if (output.label_size() > 0) {
+      if (input.has_location_data()) {
+        *output.mutable_location_data() = input.location_data();
+      }
+      Detection output_sorted;
+      if (!SortLabelsByDecreasingScore(output, &output_sorted).ok()) {
+        // Uses the orginal output if fails to sort.
+        cc->GetCounter("FailedToSortLabelsInDetection")->Increment();
+        output_sorted = output;
+      }
+      outputs->emplace_back(output_sorted);
+    }
+  }
+
+  if (cc->Outputs().HasTag(kDetectionsTag)) {
+    cc->Outputs()
+        .Tag(kDetectionsTag)
+        .Add(outputs.release(), cc->InputTimestamp());
+  } else if (!outputs->empty()) {
+    cc->Outputs()
+        .Tag(kDetectionsTag)
+        .Add(new Detection((*outputs)[0]), cc->InputTimestamp());
+  }
+  return absl::OkStatus();
+}
+
+bool FilterDetectionCalculator::IsValidLabel(const std::string& label) {
+  bool match = !limit_labels_ || allowed_labels_.contains(label);
+  if (!match) {
+    // If no exact match is found, check for regular expression
+    // comparions in the allowed_labels.
+    for (const auto& label_regexp : allowed_labels_) {
+      match = match || RE2::FullMatch(label, RE2(label_regexp));
+    }
+  }
+  return match;
+}
+
+bool FilterDetectionCalculator::IsValidScore(float score) {
+  if (options_.has_min_score() && score < options_.min_score()) {
+    LOG(ERROR) << "Filter out detection with low score " << score;
+    return false;
+  }
+  if (options_.has_max_score() && score > options_.max_score()) {
+    LOG(ERROR) << "Filter out detection with high score " << score;
+    return false;
+  }
+  return true;
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/filter_detection_calculator.proto b/mediapipe/modules/objectron/calculators/filter_detection_calculator.proto
new file mode 100644
index 0000000..ea79b8d
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/filter_detection_calculator.proto
@@ -0,0 +1,45 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message FilterDetectionCalculatorOptions {
+  extend CalculatorOptions {
+    optional FilterDetectionCalculatorOptions ext = 339582987;
+  }
+  optional float min_score = 1;
+  optional float max_score = 2;
+  // Setting fail_on_empty_labels to true will cause the calculator to return a
+  // failure status on Open() if an empty list is provided on the external
+  // input, immediately terminating the graph run.
+  optional bool fail_on_empty_labels = 3 [default = false];
+  // If fail_on_empty_labels is set to false setting
+  // empty_allowed_labels_means_allow_everything to
+  // false will cause the calculator to close output stream and ignore remaining
+  // inputs if an empty list is provided. If
+  // empty_allowed_labels_means_allow_everything is set to true this will force
+  // calculator to pass all labels.
+  optional bool empty_allowed_labels_means_allow_everything = 6
+      [default = false];
+  // Determines whether the input format is a vector<Detection> (use-case object
+  // detectors) or Detection (use-case classifiers).
+  optional bool use_detection_vector = 4 [deprecated = true];
+  // Determines whether the input side packet format is a vector of labels, or
+  // a string with comma separated labels.
+  optional bool use_allowed_labels_csv = 5 [deprecated = true];
+}
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.cc b/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.cc
new file mode 100644
index 0000000..476f8cb
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.cc
@@ -0,0 +1,177 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+
+#include <cmath>
+#include <vector>
+
+#include "Eigen/Dense"
+#include "absl/memory/memory.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/rect.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.pb.h"
+
+namespace mediapipe {
+
+using Matrix3fRM = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
+using Eigen::Vector2f;
+using Eigen::Vector3f;
+
+namespace {
+
+constexpr char kInputFrameAnnotationTag[] = "FRAME_ANNOTATION";
+constexpr char kOutputNormRectsTag[] = "NORM_RECTS";
+
+}  // namespace
+
+// A calculator that converts FrameAnnotation proto to NormalizedRect.
+// The rotation angle of the NormalizedRect is derived from object's 3d pose.
+// The angle is calculated such that after rotation the 2d projection of y-axis.
+// on the image plane is always vertical.
+class FrameAnnotationToRectCalculator : public CalculatorBase {
+ public:
+  enum ViewStatus {
+    TOP_VIEW_ON,
+    TOP_VIEW_OFF,
+  };
+
+  static absl::Status GetContract(CalculatorContract* cc);
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  void AddAnnotationToRect(const ObjectAnnotation& annotation,
+                           std::vector<NormalizedRect>* rect);
+  float RotationAngleFromAnnotation(const ObjectAnnotation& annotation);
+
+  float RotationAngleFromPose(const Matrix3fRM& rotation,
+                              const Vector3f& translation, const Vector3f& vec);
+  ViewStatus status_;
+  float off_threshold_;
+  float on_threshold_;
+};
+REGISTER_CALCULATOR(FrameAnnotationToRectCalculator);
+
+absl::Status FrameAnnotationToRectCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(!cc->Inputs().GetTags().empty());
+  RET_CHECK(!cc->Outputs().GetTags().empty());
+
+  if (cc->Inputs().HasTag(kInputFrameAnnotationTag)) {
+    cc->Inputs().Tag(kInputFrameAnnotationTag).Set<FrameAnnotation>();
+  }
+
+  if (cc->Outputs().HasTag(kOutputNormRectsTag)) {
+    cc->Outputs().Tag(kOutputNormRectsTag).Set<std::vector<NormalizedRect>>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationToRectCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  status_ = TOP_VIEW_OFF;
+  const auto& options = cc->Options<FrameAnnotationToRectCalculatorOptions>();
+  off_threshold_ = options.off_threshold();
+  on_threshold_ = options.on_threshold();
+  RET_CHECK(off_threshold_ <= on_threshold_);
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationToRectCalculator::Process(CalculatorContext* cc) {
+  if (cc->Inputs().Tag(kInputFrameAnnotationTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+  auto output_rects = absl::make_unique<std::vector<NormalizedRect>>();
+  const auto& frame_annotation =
+      cc->Inputs().Tag(kInputFrameAnnotationTag).Get<FrameAnnotation>();
+  for (const auto& object_annotation : frame_annotation.annotations()) {
+    AddAnnotationToRect(object_annotation, output_rects.get());
+  }
+
+  // Output.
+  cc->Outputs()
+      .Tag(kOutputNormRectsTag)
+      .Add(output_rects.release(), cc->InputTimestamp());
+  return absl::OkStatus();
+}
+
+void FrameAnnotationToRectCalculator::AddAnnotationToRect(
+    const ObjectAnnotation& annotation, std::vector<NormalizedRect>* rects) {
+  float x_min = std::numeric_limits<float>::max();
+  float x_max = std::numeric_limits<float>::min();
+  float y_min = std::numeric_limits<float>::max();
+  float y_max = std::numeric_limits<float>::min();
+  for (const auto& keypoint : annotation.keypoints()) {
+    const auto& point_2d = keypoint.point_2d();
+    x_min = std::min(x_min, point_2d.x());
+    x_max = std::max(x_max, point_2d.x());
+    y_min = std::min(y_min, point_2d.y());
+    y_max = std::max(y_max, point_2d.y());
+  }
+  NormalizedRect new_rect;
+  new_rect.set_x_center((x_min + x_max) / 2);
+  new_rect.set_y_center((y_min + y_max) / 2);
+  new_rect.set_width(x_max - x_min);
+  new_rect.set_height(y_max - y_min);
+  new_rect.set_rotation(RotationAngleFromAnnotation(annotation));
+  rects->push_back(new_rect);
+}
+
+float FrameAnnotationToRectCalculator::RotationAngleFromAnnotation(
+    const ObjectAnnotation& annotation) {
+  // Get box rotation and translation from annotation.
+  const auto box_rotation =
+      Eigen::Map<const Matrix3fRM>(annotation.rotation().data());
+  const auto box_translation =
+      Eigen::Map<const Vector3f>(annotation.translation().data());
+
+  // Rotation angle to use when top-view is on(top-view on),
+  // Which will make z-axis upright after the rotation.
+  const float angle_on =
+      RotationAngleFromPose(box_rotation, box_translation, Vector3f::UnitZ());
+  // Rotation angle to use when side-view is on(top-view off),
+  // Which will make y-axis upright after the rotation.
+  const float angle_off =
+      RotationAngleFromPose(box_rotation, box_translation, Vector3f::UnitY());
+
+  // Calculate angle between z-axis and viewing ray in degrees.
+  const float view_to_z_angle = std::acos(box_rotation(2, 1)) * 180 / M_PI;
+
+  // Determine threshold based on current status,
+  // on_threshold_ is used for TOP_VIEW_ON -> TOP_VIEW_OFF transition,
+  // off_threshold_ is used for TOP_VIEW_OFF -> TOP_VIEW_ON transition.
+  const float thresh =
+      (status_ == TOP_VIEW_ON) ? on_threshold_ : off_threshold_;
+
+  // If view_to_z_angle is smaller than threshold, then top-view is on;
+  // Otherwise top-view is off.
+  status_ = (view_to_z_angle < thresh) ? TOP_VIEW_ON : TOP_VIEW_OFF;
+
+  // Determine which angle to used based on current status_.
+  float angle_to_rotate = (status_ == TOP_VIEW_ON) ? angle_on : angle_off;
+  return angle_to_rotate;
+}
+
+float FrameAnnotationToRectCalculator::RotationAngleFromPose(
+    const Matrix3fRM& rotation, const Vector3f& translation,
+    const Vector3f& vec) {
+  auto p1 = rotation * vec + translation;
+  auto p2 = -rotation * vec + translation;
+  const float dy = p2[2] * p2[1] - p1[2] * p1[1];
+  const float dx = p2[2] * p2[0] - p1[2] * p1[0];
+  return M_PI / 2 - std::atan2(dy, dx);
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.proto b/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.proto
new file mode 100644
index 0000000..8959cb8
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.proto
@@ -0,0 +1,31 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message FrameAnnotationToRectCalculatorOptions {
+  extend CalculatorOptions {
+    optional FrameAnnotationToRectCalculatorOptions ext = 338119067;
+  }
+
+  // The threshold to use when top-view is off,to enable hysteresis,
+  // It's required that off_threshold <= on_threshold.
+  optional float off_threshold = 1 [default = 40.0];
+  // The threshold to use when top-view is on.
+  optional float on_threshold = 2 [default = 41.0];
+}
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_to_timed_box_list_calculator.cc b/mediapipe/modules/objectron/calculators/frame_annotation_to_timed_box_list_calculator.cc
new file mode 100644
index 0000000..7467880
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_to_timed_box_list_calculator.cc
@@ -0,0 +1,115 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <utility>
+
+#include "absl/memory/memory.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/framework/port/opencv_imgproc_inc.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/box_util.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace {
+constexpr char kInputStreamTag[] = "FRAME_ANNOTATION";
+constexpr char kOutputStreamTag[] = "BOXES";
+}  // namespace
+
+namespace mediapipe {
+
+// Convert FrameAnnotation 3d bounding box detections to TimedBoxListProto
+// 2d bounding boxes.
+//
+// Input:
+//  FRAME_ANNOTATION - 3d bounding box annotation.
+// Output:
+//  BOXES - 2d bounding box enclosing the projection of 3d box.
+//
+// Usage example:
+// node {
+//   calculator: "FrameAnnotationToTimedBoxListCalculator"
+//   input_stream: "FRAME_ANNOTATION:frame_annotation"
+//   output_stream: "BOXES:boxes"
+// }
+class FrameAnnotationToTimedBoxListCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
+};
+REGISTER_CALCULATOR(FrameAnnotationToTimedBoxListCalculator);
+
+absl::Status FrameAnnotationToTimedBoxListCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(!cc->Inputs().GetTags().empty());
+  RET_CHECK(!cc->Outputs().GetTags().empty());
+
+  if (cc->Inputs().HasTag(kInputStreamTag)) {
+    cc->Inputs().Tag(kInputStreamTag).Set<FrameAnnotation>();
+  }
+
+  if (cc->Outputs().HasTag(kOutputStreamTag)) {
+    cc->Outputs().Tag(kOutputStreamTag).Set<TimedBoxProtoList>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationToTimedBoxListCalculator::Open(
+    CalculatorContext* cc) {
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationToTimedBoxListCalculator::Process(
+    CalculatorContext* cc) {
+  if (cc->Inputs().HasTag(kInputStreamTag) &&
+      !cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
+    const auto& frame_annotation =
+        cc->Inputs().Tag(kInputStreamTag).Get<FrameAnnotation>();
+    auto output_objects = absl::make_unique<TimedBoxProtoList>();
+    for (const auto& annotation : frame_annotation.annotations()) {
+      std::vector<cv::Point2f> key_points;
+      for (const auto& keypoint : annotation.keypoints()) {
+        key_points.push_back(
+            cv::Point2f(keypoint.point_2d().x(), keypoint.point_2d().y()));
+      }
+      TimedBoxProto* added_box = output_objects->add_box();
+      ComputeBoundingRect(key_points, added_box);
+      added_box->set_id(annotation.object_id());
+      const int64 time_msec =
+          static_cast<int64>(std::round(frame_annotation.timestamp() / 1000));
+      added_box->set_time_msec(time_msec);
+    }
+
+    // Output
+    if (cc->Outputs().HasTag(kOutputStreamTag)) {
+      cc->Outputs()
+          .Tag(kOutputStreamTag)
+          .Add(output_objects.release(), cc->InputTimestamp());
+    }
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationToTimedBoxListCalculator::Close(
+    CalculatorContext* cc) {
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc b/mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc
new file mode 100644
index 0000000..eebf885
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc
@@ -0,0 +1,102 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker.h"
+
+#include "absl/container/flat_hash_set.h"
+#include "mediapipe/framework/port/logging.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/box_util.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace mediapipe {
+
+void FrameAnnotationTracker::AddDetectionResult(
+    const FrameAnnotation& frame_annotation) {
+  const int64 time_us =
+      static_cast<int64>(std::round(frame_annotation.timestamp()));
+  for (const auto& object_annotation : frame_annotation.annotations()) {
+    detected_objects_[time_us + object_annotation.object_id()] =
+        object_annotation;
+  }
+}
+
+FrameAnnotation FrameAnnotationTracker::ConsolidateTrackingResult(
+    const TimedBoxProtoList& tracked_boxes,
+    absl::flat_hash_set<int>* cancel_object_ids) {
+  CHECK(cancel_object_ids != nullptr);
+  FrameAnnotation frame_annotation;
+  std::vector<int64> keys_to_be_deleted;
+  for (const auto& detected_obj : detected_objects_) {
+    const int object_id = detected_obj.second.object_id();
+    if (cancel_object_ids->contains(object_id)) {
+      // Remember duplicated detections' keys.
+      keys_to_be_deleted.push_back(detected_obj.first);
+      continue;
+    }
+    TimedBoxProto ref_box;
+    for (const auto& box : tracked_boxes.box()) {
+      if (box.id() == object_id) {
+        ref_box = box;
+        break;
+      }
+    }
+    if (!ref_box.has_id() || ref_box.id() < 0) {
+      LOG(ERROR) << "Can't find matching tracked box for object id: "
+                 << object_id << ". Likely lost tracking of it.";
+      keys_to_be_deleted.push_back(detected_obj.first);
+      continue;
+    }
+
+    // Find duplicated boxes
+    for (const auto& box : tracked_boxes.box()) {
+      if (box.id() != object_id) {
+        if (ComputeBoxIoU(ref_box, box) > iou_threshold_) {
+          cancel_object_ids->insert(box.id());
+        }
+      }
+    }
+
+    // Map ObjectAnnotation from detection to tracked time.
+    // First, gather all keypoints from source detection.
+    std::vector<cv::Point2f> key_points;
+    for (const auto& keypoint : detected_obj.second.keypoints()) {
+      key_points.push_back(
+          cv::Point2f(keypoint.point_2d().x(), keypoint.point_2d().y()));
+    }
+    // Second, find source box.
+    TimedBoxProto src_box;
+    ComputeBoundingRect(key_points, &src_box);
+    ObjectAnnotation* tracked_obj = frame_annotation.add_annotations();
+    tracked_obj->set_object_id(ref_box.id());
+    // Finally, map all keypoints in the source detection to tracked location.
+    for (const auto& keypoint : detected_obj.second.keypoints()) {
+      cv::Point2f dst = MapPoint(
+          src_box, ref_box,
+          cv::Point2f(keypoint.point_2d().x(), keypoint.point_2d().y()),
+          img_width_, img_height_);
+      auto* dst_point = tracked_obj->add_keypoints()->mutable_point_2d();
+      dst_point->set_x(dst.x);
+      dst_point->set_y(dst.y);
+    }
+  }
+
+  for (const auto& key : keys_to_be_deleted) {
+    detected_objects_.erase(key);
+  }
+
+  return frame_annotation;
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker.h b/mediapipe/modules/objectron/calculators/frame_annotation_tracker.h
new file mode 100644
index 0000000..11a469c
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_tracker.h
@@ -0,0 +1,62 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_FRAME_ANNOTATION_TRACKER_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_FRAME_ANNOTATION_TRACKER_H_
+
+#include <functional>
+
+#include "absl/container/btree_map.h"
+#include "absl/container/flat_hash_set.h"
+#include "mediapipe/framework/port/integral_types.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace mediapipe {
+
+class FrameAnnotationTracker {
+ public:
+  // If two bounding boxes have IoU over iou_threshold, then we consider them
+  // describing the same object.
+  FrameAnnotationTracker(float iou_threshold, float img_width, float img_height)
+      : iou_threshold_(iou_threshold),
+        img_width_(img_width),
+        img_height_(img_height) {}
+
+  // Adds detection results from an external detector.
+  void AddDetectionResult(const FrameAnnotation& frame_annotation);
+
+  // Consolidates tracking result from an external tracker, associates with
+  // the detection result by the object id, and produces the corresponding
+  // result in FrameAnnotation. When there are duplicates, output the ids that
+  // need to be cancelled in cancel_object_ids.
+  // Note that the returned FrameAnnotation is missing timestamp. Need to fill
+  // that field.
+  FrameAnnotation ConsolidateTrackingResult(
+      const TimedBoxProtoList& tracked_boxes,
+      absl::flat_hash_set<int>* cancel_object_ids);
+
+ private:
+  float iou_threshold_;
+  float img_width_;
+  float img_height_;
+  // Cached detection results over time.
+  // Key is timestamp_us + object_id.
+  absl::btree_map<int64, ObjectAnnotation, std::greater<int64>>
+      detected_objects_;
+};
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_FRAME_ANNOTATION_TRACKER_H_
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.cc b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.cc
new file mode 100644
index 0000000..9079b9a
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.cc
@@ -0,0 +1,134 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "absl/container/flat_hash_set.h"
+#include "absl/memory/memory.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker.h"
+#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.pb.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace {
+constexpr char kInputFrameAnnotationTag[] = "FRAME_ANNOTATION";
+constexpr char kInputTrackedBoxesTag[] = "TRACKED_BOXES";
+constexpr char kOutputTrackedFrameAnnotationTag[] = "TRACKED_FRAME_ANNOTATION";
+constexpr char kOutputCancelObjectIdTag[] = "CANCEL_OBJECT_ID";
+}  // namespace
+
+namespace mediapipe {
+
+// Tracks frame annotations seeded/updated by FRAME_ANNOTATION input_stream.
+// When using this calculator, make sure FRAME_ANNOTATION and TRACKED_BOXES
+// are in different sync set.
+//
+// Input:
+//  FRAME_ANNOTATION - frame annotation.
+//  TRACKED_BOXES - 2d box tracking result
+// Output:
+//  TRACKED_FRAME_ANNOTATION - annotation inferred from 2d tracking result.
+//  CANCEL_OBJECT_ID - object id that needs to be cancelled from the tracker.
+//
+// Usage example:
+// node {
+//   calculator: "FrameAnnotationTrackerCalculator"
+//   input_stream: "FRAME_ANNOTATION:frame_annotation"
+//   input_stream: "TRACKED_BOXES:tracked_boxes"
+//   output_stream: "TRACKED_FRAME_ANNOTATION:tracked_frame_annotation"
+//   output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
+// }
+class FrameAnnotationTrackerCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
+
+ private:
+  std::unique_ptr<FrameAnnotationTracker> frame_annotation_tracker_;
+};
+REGISTER_CALCULATOR(FrameAnnotationTrackerCalculator);
+
+absl::Status FrameAnnotationTrackerCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(!cc->Inputs().GetTags().empty());
+  RET_CHECK(!cc->Outputs().GetTags().empty());
+
+  if (cc->Inputs().HasTag(kInputFrameAnnotationTag)) {
+    cc->Inputs().Tag(kInputFrameAnnotationTag).Set<FrameAnnotation>();
+  }
+  if (cc->Inputs().HasTag(kInputTrackedBoxesTag)) {
+    cc->Inputs().Tag(kInputTrackedBoxesTag).Set<TimedBoxProtoList>();
+  }
+  if (cc->Outputs().HasTag(kOutputTrackedFrameAnnotationTag)) {
+    cc->Outputs().Tag(kOutputTrackedFrameAnnotationTag).Set<FrameAnnotation>();
+  }
+  if (cc->Outputs().HasTag(kOutputCancelObjectIdTag)) {
+    cc->Outputs().Tag(kOutputCancelObjectIdTag).Set<int>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationTrackerCalculator::Open(CalculatorContext* cc) {
+  const auto& options = cc->Options<FrameAnnotationTrackerCalculatorOptions>();
+  frame_annotation_tracker_ = absl::make_unique<FrameAnnotationTracker>(
+      options.iou_threshold(), options.img_width(), options.img_height());
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationTrackerCalculator::Process(CalculatorContext* cc) {
+  if (cc->Inputs().HasTag(kInputFrameAnnotationTag) &&
+      !cc->Inputs().Tag(kInputFrameAnnotationTag).IsEmpty()) {
+    frame_annotation_tracker_->AddDetectionResult(
+        cc->Inputs().Tag(kInputFrameAnnotationTag).Get<FrameAnnotation>());
+  }
+  if (cc->Inputs().HasTag(kInputTrackedBoxesTag) &&
+      !cc->Inputs().Tag(kInputTrackedBoxesTag).IsEmpty() &&
+      cc->Outputs().HasTag(kOutputTrackedFrameAnnotationTag)) {
+    absl::flat_hash_set<int> cancel_object_ids;
+    auto output_frame_annotation = absl::make_unique<FrameAnnotation>();
+    *output_frame_annotation =
+        frame_annotation_tracker_->ConsolidateTrackingResult(
+            cc->Inputs().Tag(kInputTrackedBoxesTag).Get<TimedBoxProtoList>(),
+            &cancel_object_ids);
+    output_frame_annotation->set_timestamp(cc->InputTimestamp().Microseconds());
+
+    cc->Outputs()
+        .Tag(kOutputTrackedFrameAnnotationTag)
+        .Add(output_frame_annotation.release(), cc->InputTimestamp());
+
+    if (cc->Outputs().HasTag(kOutputCancelObjectIdTag)) {
+      auto packet_timestamp = cc->InputTimestamp();
+      for (const auto& id : cancel_object_ids) {
+        // The timestamp is incremented (by 1 us) because currently the box
+        // tracker calculator only accepts one cancel object ID for any given
+        // timestamp.
+        cc->Outputs()
+            .Tag(kOutputCancelObjectIdTag)
+            .AddPacket(mediapipe::MakePacket<int>(id).At(packet_timestamp++));
+      }
+    }
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status FrameAnnotationTrackerCalculator::Close(CalculatorContext* cc) {
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.proto b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.proto
new file mode 100644
index 0000000..f37308a
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.proto
@@ -0,0 +1,36 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The option proto for the FrameAnnotationTrackerCalculatorOptions.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message FrameAnnotationTrackerCalculatorOptions {
+  extend CalculatorOptions {
+    optional FrameAnnotationTrackerCalculatorOptions ext = 291291253;
+  }
+
+  // The threshold on intersection-over-union (IoU). We consider
+  // boxes with IoU larger than this threshold to be the duplicates.
+  optional float iou_threshold = 1 [default = 0.5];
+
+  // We need image dimension to properly compute annotation locations.
+  optional float img_width = 2;
+
+  optional float img_height = 3;
+}
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_test.cc b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_test.cc
new file mode 100644
index 0000000..d155f8e
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_test.cc
@@ -0,0 +1,143 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker.h"
+
+#include "absl/container/flat_hash_set.h"
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/logging.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/util/tracking/box_tracker.pb.h"
+
+namespace mediapipe {
+namespace {
+
+// Create a new object annotation by shifting a reference
+// object annotation.
+ObjectAnnotation ShiftObject2d(const ObjectAnnotation& ref_obj, float dx,
+                               float dy) {
+  ObjectAnnotation obj = ref_obj;
+  for (auto& keypoint : *(obj.mutable_keypoints())) {
+    const float ref_x = keypoint.point_2d().x();
+    const float ref_y = keypoint.point_2d().y();
+    keypoint.mutable_point_2d()->set_x(ref_x + dx);
+    keypoint.mutable_point_2d()->set_y(ref_y + dy);
+  }
+  return obj;
+}
+
+TimedBoxProto ShiftBox(const TimedBoxProto& ref_box, float dx, float dy) {
+  TimedBoxProto box = ref_box;
+  box.set_top(ref_box.top() + dy);
+  box.set_bottom(ref_box.bottom() + dy);
+  box.set_left(ref_box.left() + dx);
+  box.set_right(ref_box.right() + dx);
+  return box;
+}
+
+// Constructs a fixed ObjectAnnotation.
+ObjectAnnotation ConstructFixedObject(
+    const std::vector<std::vector<float>>& points) {
+  ObjectAnnotation obj;
+  for (const auto& point : points) {
+    auto* keypoint = obj.add_keypoints();
+    CHECK_EQ(2, point.size());
+    keypoint->mutable_point_2d()->set_x(point[0]);
+    keypoint->mutable_point_2d()->set_y(point[1]);
+  }
+  return obj;
+}
+
+TEST(FrameAnnotationTrackerTest, TestConsolidation) {
+  // Add 4 detections represented by FrameAnnotation, of which 3 correspond
+  // to the same object.
+  ObjectAnnotation object1, object2, object3, object4;
+  // The bounding rectangle for these object keypoints is:
+  // x: [0.2, 0.5], y: [0.1, 0.4]
+  object3 = ConstructFixedObject({{0.35f, 0.25f},
+                                  {0.3f, 0.3f},
+                                  {0.2f, 0.4f},
+                                  {0.3f, 0.1f},
+                                  {0.2f, 0.2f},
+                                  {0.5f, 0.3f},
+                                  {0.4f, 0.4f},
+                                  {0.5f, 0.1f},
+                                  {0.4f, 0.2f}});
+  object3.set_object_id(3);
+  object1 = ShiftObject2d(object3, -0.05f, -0.05f);
+  object1.set_object_id(1);
+  object2 = ShiftObject2d(object3, 0.05f, 0.05f);
+  object2.set_object_id(2);
+  object4 = ShiftObject2d(object3, 0.2f, 0.2f);
+  object4.set_object_id(4);
+  FrameAnnotation frame_annotation_1;
+  frame_annotation_1.set_timestamp(30 * 1000);  // 30ms
+  *(frame_annotation_1.add_annotations()) = object1;
+  *(frame_annotation_1.add_annotations()) = object4;
+  FrameAnnotation frame_annotation_2;
+  frame_annotation_2.set_timestamp(60 * 1000);  // 60ms
+  *(frame_annotation_2.add_annotations()) = object2;
+  FrameAnnotation frame_annotation_3;
+  frame_annotation_3.set_timestamp(90 * 1000);  // 90ms
+  *(frame_annotation_3.add_annotations()) = object3;
+
+  FrameAnnotationTracker frame_annotation_tracker(/*iou_threshold*/ 0.5f, 1.0f,
+                                                  1.0f);
+  frame_annotation_tracker.AddDetectionResult(frame_annotation_1);
+  frame_annotation_tracker.AddDetectionResult(frame_annotation_2);
+  frame_annotation_tracker.AddDetectionResult(frame_annotation_3);
+
+  TimedBoxProtoList timed_box_proto_list;
+  TimedBoxProto* timed_box_proto = timed_box_proto_list.add_box();
+  timed_box_proto->set_top(0.4f);
+  timed_box_proto->set_bottom(0.7f);
+  timed_box_proto->set_left(0.6f);
+  timed_box_proto->set_right(0.9f);
+  timed_box_proto->set_id(3);
+  timed_box_proto->set_time_msec(150);
+  timed_box_proto = timed_box_proto_list.add_box();
+  *timed_box_proto = ShiftBox(timed_box_proto_list.box(0), 0.01f, 0.01f);
+  timed_box_proto->set_id(1);
+  timed_box_proto->set_time_msec(150);
+  timed_box_proto = timed_box_proto_list.add_box();
+  *timed_box_proto = ShiftBox(timed_box_proto_list.box(0), -0.01f, -0.01f);
+  timed_box_proto->set_id(2);
+  timed_box_proto->set_time_msec(150);
+  absl::flat_hash_set<int> cancel_object_ids;
+  FrameAnnotation tracked_detection =
+      frame_annotation_tracker.ConsolidateTrackingResult(timed_box_proto_list,
+                                                         &cancel_object_ids);
+  EXPECT_EQ(2, cancel_object_ids.size());
+  EXPECT_EQ(1, cancel_object_ids.count(1));
+  EXPECT_EQ(1, cancel_object_ids.count(2));
+  EXPECT_EQ(1, tracked_detection.annotations_size());
+  EXPECT_EQ(3, tracked_detection.annotations(0).object_id());
+  EXPECT_EQ(object3.keypoints_size(),
+            tracked_detection.annotations(0).keypoints_size());
+  const float x_offset = 0.4f;
+  const float y_offset = 0.3f;
+  const float tolerance = 1e-5f;
+  for (int i = 0; i < object3.keypoints_size(); ++i) {
+    const auto& point_2d =
+        tracked_detection.annotations(0).keypoints(i).point_2d();
+    EXPECT_NEAR(point_2d.x(), object3.keypoints(i).point_2d().x() + x_offset,
+                tolerance);
+    EXPECT_NEAR(point_2d.y(), object3.keypoints(i).point_2d().y() + y_offset,
+                tolerance);
+  }
+}
+
+}  // namespace
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/landmarks_to_frame_annotation_calculator.cc b/mediapipe/modules/objectron/calculators/landmarks_to_frame_annotation_calculator.cc
new file mode 100644
index 0000000..60c4876
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/landmarks_to_frame_annotation_calculator.cc
@@ -0,0 +1,112 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+
+#include "absl/memory/memory.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kInputLandmarksTag[] = "LANDMARKS";
+constexpr char kInputMultiLandmarksTag[] = "MULTI_LANDMARKS";
+constexpr char kOutputFrameAnnotationTag[] = "FRAME_ANNOTATION";
+
+}  // namespace
+
+// A calculator that converts NormalizedLandmarkList to FrameAnnotation proto.
+class LandmarksToFrameAnnotationCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+
+ private:
+  void AddLandmarksToFrameAnnotation(const NormalizedLandmarkList& landmarks,
+                                     FrameAnnotation* frame_annotation);
+};
+REGISTER_CALCULATOR(LandmarksToFrameAnnotationCalculator);
+
+absl::Status LandmarksToFrameAnnotationCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(!cc->Inputs().GetTags().empty());
+  RET_CHECK(!cc->Outputs().GetTags().empty());
+
+  if (cc->Inputs().HasTag(kInputLandmarksTag)) {
+    cc->Inputs().Tag(kInputLandmarksTag).Set<NormalizedLandmarkList>();
+  }
+  if (cc->Inputs().HasTag(kInputMultiLandmarksTag)) {
+    cc->Inputs()
+        .Tag(kInputMultiLandmarksTag)
+        .Set<std::vector<NormalizedLandmarkList>>();
+  }
+  if (cc->Outputs().HasTag(kOutputFrameAnnotationTag)) {
+    cc->Outputs().Tag(kOutputFrameAnnotationTag).Set<FrameAnnotation>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status LandmarksToFrameAnnotationCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  return absl::OkStatus();
+}
+
+absl::Status LandmarksToFrameAnnotationCalculator::Process(
+    CalculatorContext* cc) {
+  auto frame_annotation = absl::make_unique<FrameAnnotation>();
+
+  // Handle the case when input has only one NormalizedLandmarkList.
+  if (cc->Inputs().HasTag(kInputLandmarksTag) &&
+      !cc->Inputs().Tag(kInputLandmarksTag).IsEmpty()) {
+    const auto& landmarks =
+        cc->Inputs().Tag(kInputMultiLandmarksTag).Get<NormalizedLandmarkList>();
+    AddLandmarksToFrameAnnotation(landmarks, frame_annotation.get());
+  }
+
+  // Handle the case when input has muliple NormalizedLandmarkList.
+  if (cc->Inputs().HasTag(kInputMultiLandmarksTag) &&
+      !cc->Inputs().Tag(kInputMultiLandmarksTag).IsEmpty()) {
+    const auto& landmarks_list =
+        cc->Inputs()
+            .Tag(kInputMultiLandmarksTag)
+            .Get<std::vector<NormalizedLandmarkList>>();
+    for (const auto& landmarks : landmarks_list) {
+      AddLandmarksToFrameAnnotation(landmarks, frame_annotation.get());
+    }
+  }
+
+  // Output
+  if (cc->Outputs().HasTag(kOutputFrameAnnotationTag)) {
+    cc->Outputs()
+        .Tag(kOutputFrameAnnotationTag)
+        .Add(frame_annotation.release(), cc->InputTimestamp());
+  }
+  return absl::OkStatus();
+}
+
+void LandmarksToFrameAnnotationCalculator::AddLandmarksToFrameAnnotation(
+    const NormalizedLandmarkList& landmarks,
+    FrameAnnotation* frame_annotation) {
+  auto* new_annotation = frame_annotation->add_annotations();
+  for (const auto& landmark : landmarks.landmark()) {
+    auto* point2d = new_annotation->add_keypoints()->mutable_point_2d();
+    point2d->set_x(landmark.x());
+    point2d->set_y(landmark.y());
+  }
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.cc b/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.cc
new file mode 100644
index 0000000..1405e5a
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.cc
@@ -0,0 +1,169 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <memory>
+#include <unordered_map>
+#include <vector>
+
+#include "Eigen/Dense"
+#include "absl/memory/memory.h"
+#include "absl/strings/str_format.h"
+#include "absl/types/span.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/deps/file_path.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/decoder.h"
+#include "mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.pb.h"
+#include "mediapipe/modules/objectron/calculators/tensor_util.h"
+
+namespace {
+constexpr char kInputStreamTag[] = "FRAME_ANNOTATION";
+constexpr char kOutputStreamTag[] = "LIFTED_FRAME_ANNOTATION";
+
+// Each detection object will be assigned an unique id that starts from 1.
+static int object_id = 0;
+
+inline int GetNextObjectId() { return ++object_id; }
+}  // namespace
+
+namespace mediapipe {
+
+// Lifted the 2D points in a tracked frame annotation to 3D.
+//
+// Input:
+//  FRAME_ANNOTATIONS - Frame annotations with detected 2D points
+// Output:
+//  LIFTED_FRAME_ANNOTATIONS - Result FrameAnnotation with lifted 3D points.
+//
+// Usage example:
+// node {
+//   calculator: "Lift2DFrameAnnotationTo3DCalculator"
+//   input_stream: "FRAME_ANNOTATIONS:tracked_annotations"
+//   output_stream: "LIFTED_FRAME_ANNOTATIONS:lifted_3d_annotations"
+// }
+class Lift2DFrameAnnotationTo3DCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
+
+ private:
+  absl::Status ProcessCPU(CalculatorContext* cc,
+                          FrameAnnotation* output_objects);
+  absl::Status LoadOptions(CalculatorContext* cc);
+
+  // Increment and assign object ID for each detected object.
+  // In a single MediaPipe session, the IDs are unique.
+  // Also assign timestamp for the FrameAnnotation to be the input packet
+  // timestamp.
+  void AssignObjectIdAndTimestamp(int64 timestamp_us,
+                                  FrameAnnotation* annotation);
+  std::unique_ptr<Decoder> decoder_;
+  Lift2DFrameAnnotationTo3DCalculatorOptions options_;
+  Eigen::Matrix<float, 4, 4, Eigen::RowMajor> projection_matrix_;
+};
+REGISTER_CALCULATOR(Lift2DFrameAnnotationTo3DCalculator);
+
+absl::Status Lift2DFrameAnnotationTo3DCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(cc->Inputs().HasTag(kInputStreamTag));
+  RET_CHECK(cc->Outputs().HasTag(kOutputStreamTag));
+  cc->Inputs().Tag(kInputStreamTag).Set<FrameAnnotation>();
+  cc->Outputs().Tag(kOutputStreamTag).Set<FrameAnnotation>();
+
+  return absl::OkStatus();
+}
+
+absl::Status Lift2DFrameAnnotationTo3DCalculator::Open(CalculatorContext* cc) {
+  cc->SetOffset(TimestampDiff(0));
+  MP_RETURN_IF_ERROR(LoadOptions(cc));
+  // Load camera intrinsic matrix.
+  const float fx = options_.normalized_focal_x();
+  const float fy = options_.normalized_focal_y();
+  const float px = options_.normalized_principal_point_x();
+  const float py = options_.normalized_principal_point_y();
+  // clang-format off
+  projection_matrix_ << fx, 0.,  px, 0.,
+                        0., fy,  py, 0.,
+                        0., 0., -1., 0.,
+                        0., 0., -1., 0.;
+  // clang-format on
+  decoder_ = absl::make_unique<Decoder>(
+      BeliefDecoderConfig(options_.decoder_config()));
+  return absl::OkStatus();
+}
+
+absl::Status Lift2DFrameAnnotationTo3DCalculator::Process(
+    CalculatorContext* cc) {
+  if (cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+
+  auto output_objects = absl::make_unique<FrameAnnotation>();
+
+  MP_RETURN_IF_ERROR(ProcessCPU(cc, output_objects.get()));
+
+  // Output
+  if (cc->Outputs().HasTag(kOutputStreamTag)) {
+    cc->Outputs()
+        .Tag(kOutputStreamTag)
+        .Add(output_objects.release(), cc->InputTimestamp());
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status Lift2DFrameAnnotationTo3DCalculator::ProcessCPU(
+    CalculatorContext* cc, FrameAnnotation* output_objects) {
+  const auto& input_frame_annotations =
+      cc->Inputs().Tag(kInputStreamTag).Get<FrameAnnotation>();
+  // Copy the input frame annotation to the output
+  *output_objects = input_frame_annotations;
+
+  auto status = decoder_->Lift2DTo3D(projection_matrix_, /*portrait*/ false,
+                                     output_objects);
+  if (!status.ok()) {
+    LOG(ERROR) << status;
+    return status;
+  }
+  AssignObjectIdAndTimestamp(cc->InputTimestamp().Microseconds(),
+                             output_objects);
+
+  return absl::OkStatus();
+}
+
+absl::Status Lift2DFrameAnnotationTo3DCalculator::Close(CalculatorContext* cc) {
+  return absl::OkStatus();
+}
+
+absl::Status Lift2DFrameAnnotationTo3DCalculator::LoadOptions(
+    CalculatorContext* cc) {
+  // Get calculator options specified in the graph.
+  options_ = cc->Options<Lift2DFrameAnnotationTo3DCalculatorOptions>();
+
+  return absl::OkStatus();
+}
+
+void Lift2DFrameAnnotationTo3DCalculator::AssignObjectIdAndTimestamp(
+    int64 timestamp_us, FrameAnnotation* annotation) {
+  for (auto& ann : *annotation->mutable_annotations()) {
+    ann.set_object_id(GetNextObjectId());
+  }
+  annotation->set_timestamp(timestamp_us);
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.proto b/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.proto
new file mode 100644
index 0000000..a3005c1
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.proto
@@ -0,0 +1,42 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The option proto for the Lift2DFrameAnnotationTo3DCalculatorOptions.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+import "mediapipe/modules/objectron/calculators/belief_decoder_config.proto";
+
+message Lift2DFrameAnnotationTo3DCalculatorOptions {
+  extend CalculatorOptions {
+    optional Lift2DFrameAnnotationTo3DCalculatorOptions ext = 290166284;
+  }
+
+  optional BeliefDecoderConfig decoder_config = 1;
+
+  // Camera focal length along x, normalized by width/2.
+  optional float normalized_focal_x = 2 [default = 1.0];
+
+  // Camera focal length along y, normalized by height/2.
+  optional float normalized_focal_y = 3 [default = 1.0];
+
+  // Camera principle point x, normalized by width/2, origin is image center.
+  optional float normalized_principal_point_x = 4 [default = 0.0];
+
+  // Camera principle point y, normalized by height/2, origin is image center.
+  optional float normalized_principal_point_y = 5 [default = 0.0];
+}
diff --git a/mediapipe/modules/objectron/calculators/model.cc b/mediapipe/modules/objectron/calculators/model.cc
new file mode 100644
index 0000000..40aca39
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/model.cc
@@ -0,0 +1,101 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/model.h"
+
+#include "mediapipe/framework/port/logging.h"
+
+namespace mediapipe {
+
+void Model::SetTransformation(const Eigen::Matrix4f& transform) {
+  transformation_ = transform;
+}
+
+void Model::SetTranslation(const Eigen::Vector3f& translation) {
+  transformation_.col(3).template head<3>() = translation;
+}
+
+void Model::SetRotation(float roll, float pitch, float yaw) {
+  // In our coordinate system, Y is up. We first rotate the object around Y
+  // (yaw), then around Z (pitch), and finally around X (roll).
+  Eigen::Matrix3f r;
+  r = Eigen::AngleAxisf(yaw, Eigen::Vector3f::UnitY()) *
+      Eigen::AngleAxisf(pitch, Eigen::Vector3f::UnitZ()) *
+      Eigen::AngleAxisf(roll, Eigen::Vector3f::UnitX());
+  transformation_.topLeftCorner<3, 3>() = r;
+}
+
+void Model::SetRotation(const Eigen::Matrix3f& rotation) {
+  transformation_.topLeftCorner<3, 3>() = rotation;
+}
+
+void Model::SetScale(const Eigen::Vector3f& scale) { scale_ = scale; }
+
+void Model::SetCategory(const std::string& category) { category_ = category; }
+
+const Eigen::Vector3f Model::GetRotationAngles() const {
+  Vector3f ypr = transformation_.topLeftCorner<3, 3>().eulerAngles(1, 2, 0);
+  return Vector3f(ypr(2), ypr(1), ypr(0));  // swap YPR with RPY
+}
+
+const Eigen::Matrix4f& Model::GetTransformation() const {
+  return transformation_;
+}
+
+const Eigen::Vector3f& Model::GetScale() const { return scale_; }
+
+const Eigen::Ref<const Eigen::Vector3f> Model::GetTranslation() const {
+  return transformation_.col(3).template head<3>();
+}
+
+const Eigen::Ref<const Eigen::Matrix3f> Model::GetRotation() const {
+  return transformation_.template topLeftCorner<3, 3>();
+}
+
+const std::string& Model::GetCategory() const { return category_; }
+
+void Model::Deserialize(const Object& obj) {
+  CHECK_EQ(obj.rotation_size(), 9);
+  CHECK_EQ(obj.translation_size(), 3);
+  CHECK_EQ(obj.scale_size(), 3);
+  category_ = obj.category();
+
+  using RotationMatrix = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
+  transformation_.setIdentity();
+  transformation_.topLeftCorner<3, 3>() =
+      Eigen::Map<const RotationMatrix>(obj.rotation().data());
+  transformation_.col(3).head<3>() =
+      Eigen::Map<const Eigen::Vector3f>(obj.translation().data());
+  scale_ = Eigen::Map<const Eigen::Vector3f>(obj.scale().data());
+  Update();
+}
+
+void Model::Serialize(Object* obj) {
+  obj->set_category(category_);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      obj->add_rotation(transformation_(i, j));
+    }
+  }
+
+  for (int i = 0; i < 3; ++i) {
+    obj->add_translation(transformation_(i, 3));
+  }
+
+  for (int i = 0; i < 3; ++i) {
+    obj->add_scale(scale_[i]);
+  }
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/model.h b/mediapipe/modules/objectron/calculators/model.h
new file mode 100644
index 0000000..72b5eb2
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/model.h
@@ -0,0 +1,92 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_MODEL_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_MODEL_H_
+
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/object.pb.h"
+#include "mediapipe/modules/objectron/calculators/types.h"
+
+namespace mediapipe {
+
+class Model {
+ public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
+
+  enum Type {
+    kVisualizationOnly = 0,
+    kBoundingBox,
+    kSkeleton,
+    kShape,  // Shape is a virtual object.
+    kNumModes,
+  };
+
+  virtual ~Model() = default;
+
+  virtual void SetTransformation(const Eigen::Matrix4f& transform);
+  virtual void SetTranslation(const Eigen::Vector3f& translation);
+
+  // Compute the rotation matrix from these angles and update the transformation
+  // matrix accordingly
+  virtual void SetRotation(float roll, float pitch, float yaw);
+  virtual void SetRotation(const Eigen::Matrix3f& rotation);
+  virtual void SetScale(const Eigen::Vector3f& scale);
+  virtual void SetCategory(const std::string& category);
+  virtual size_t GetNumberKeypoints() const { return number_keypoints_; }
+
+  // Gets Euler angles in the order of roll, pitch, yaw.
+  virtual const Eigen::Vector3f GetRotationAngles() const;
+  virtual const Eigen::Matrix4f& GetTransformation() const;
+  virtual const Eigen::Vector3f& GetScale() const;
+  virtual const Eigen::Ref<const Eigen::Vector3f> GetTranslation() const;
+  virtual const Eigen::Ref<const Eigen::Matrix3f> GetRotation() const;
+  virtual const std::string& GetCategory() const;
+
+  // Update the model's keypoints in the world-coordinate system.
+  // The update includes transforming the model to the world-coordinate system
+  // as well as scaling the model.
+  // The user is expected to call this function after Setting the rotation,
+  // orientation or the scale of the model to get an updated model.
+  virtual void Update() = 0;
+
+  // Update the model's parameters (orientation, position, and scale) from the
+  // user-provided variables.
+  virtual void Adjust(const std::vector<float>& variables) = 0;
+
+  // Returns a pointer to the model's keypoints.
+  // Use Eigen::Map to cast the pointer back to Vector3 or Vector4
+  virtual const float* GetVertex(size_t id) const = 0;
+  virtual float* GetVertex(size_t id) = 0;
+  virtual void Deserialize(const Object& obj);
+  virtual void Serialize(Object* obj);
+
+  // TODO: make member variables protected, and add public apis.
+  // 4x4 transformation matrix mapping the first keypoint to world coordinate
+  Eigen::Matrix4f transformation_;
+  Eigen::Vector3f scale_;  // width, height, depth
+  Type model_type_;
+  size_t number_keypoints_;
+  std::string category_;
+
+ protected:
+  Model(Type type, size_t number_keypoints, const std::string& category)
+      : model_type_(type),
+        number_keypoints_(number_keypoints),
+        category_(category) {}
+};
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_MODEL_H_
diff --git a/mediapipe/modules/objectron/calculators/object.proto b/mediapipe/modules/objectron/calculators/object.proto
new file mode 100644
index 0000000..a07e83f
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/object.proto
@@ -0,0 +1,124 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto3";
+
+package mediapipe;
+
+message KeyPoint {
+  // The position of the keypoint in the local coordinate system of the rigid
+  // object.
+  float x = 1;
+  float y = 2;
+  float z = 3;
+
+  // Sphere around the keypoint, indiciating annotator's confidence of the
+  // position in meters.
+  float confidence_radius = 4;
+
+  // The name of the keypoint (e.g. legs, head, etc.).
+  // Does not have to be unique.
+  string name = 5;
+
+  // Indicates whether the keypoint is hidden or not.
+  bool hidden = 6;
+}
+
+message Object {
+  // Unique object id through a sequence. There might be multiple objects of
+  // the same label in this sequence.
+  int32 id = 1;
+
+  // Describes what category an object is. E.g. object class, attribute,
+  // instance or person identity. This provides additional context for the
+  // object type.
+  string category = 2;
+
+  enum Type {
+    UNDEFINED_TYPE = 0;
+    BOUNDING_BOX = 1;
+    SKELETON = 2;
+  }
+
+  Type type = 3;
+
+  // 3x3 row-major rotation matrix describing the orientation of the rigid
+  // object's frame of reference in the world-coordinate system.
+  repeated float rotation = 4;
+
+  // 3x1 vector describing the translation of the rigid object's frame of
+  // reference in the world-coordinate system in meters.
+  repeated float translation = 5;
+
+  // 3x1 vector describing the scale of the rigid object's frame of reference in
+  // the world-coordinate system in meters.
+  repeated float scale = 6;
+
+  // List of all the key points associated with this object in the object
+  // coordinate system.
+  // The first keypoint is always the object's frame of reference,
+  // e.g. the centroid of the box.
+  // E.g. bounding box with its center as frame of reference, the 9 keypoints :
+  // {0., 0., 0.},
+  // {-.5, -.5, -.5}, {-.5, -.5, +.5}, {-.5, +.5, -.5}, {-.5, +.5, +.5},
+  // {+.5, -.5, -.5}, {+.5, -.5, +.5}, {+.5, +.5, -.5}, {+.5, +.5, +.5}
+  // To get the bounding box in the world-coordinate system, we first scale the
+  // box then transform the scaled box.
+  // For example, bounding box in the world coordinate system is
+  // rotation * scale * keypoints + translation
+  repeated KeyPoint keypoints = 7;
+
+  // Enum to reflect how this object is created.
+  enum Method {
+    UNKNOWN_METHOD = 0;
+    ANNOTATION = 1;    // Created by data annotation.
+    AUGMENTATION = 2;  // Created by data augmentation.
+  }
+  Method method = 8;
+}
+
+// The edge connecting two keypoints together
+message Edge {
+  // keypoint id of the edge's source
+  int32 source = 1;
+
+  // keypoint id of the edge's sink
+  int32 sink = 2;
+}
+
+// The skeleton template for different objects (e.g. humans, chairs, hands, etc)
+// The annotation tool reads the skeleton template dictionary.
+message Skeleton {
+  // The origin keypoint in the object coordinate system. (i.e. Point 0, 0, 0)
+  int32 reference_keypoint = 1;
+
+  // The skeleton's category (e.g. human, chair, hand.). Should be unique in the
+  // dictionary.
+  string category = 2;
+
+  // Initialization value for all the keypoints in the skeleton in the object's
+  // local coordinate system. Pursuit will transform these points using object's
+  // transformation to get the keypoint in the world-cooridnate.
+  repeated KeyPoint keypoints = 3;
+
+  // List of edges connecting keypoints
+  repeated Edge edges = 4;
+}
+
+// The list of all the modeled skeletons in our library. These models can be
+// objects (chairs, desks, etc), humans (full pose, hands, faces, etc), or box.
+// We can have multiple skeletons in the same file.
+message Skeletons {
+  repeated Skeleton object = 1;
+}
diff --git a/mediapipe/modules/objectron/calculators/tensor_util.cc b/mediapipe/modules/objectron/calculators/tensor_util.cc
new file mode 100644
index 0000000..0004edd
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/tensor_util.cc
@@ -0,0 +1,48 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/modules/objectron/calculators/tensor_util.h"
+
+#include "mediapipe/framework/port/logging.h"
+
+namespace mediapipe {
+
+cv::Mat ConvertTfliteTensorToCvMat(const TfLiteTensor& tensor) {
+  // Check tensor is BxCxWxH (size = 4) and the batch size is one(data[0] = 1)
+  CHECK(tensor.dims->size == 4 && tensor.dims->data[0] == 1);
+  CHECK_EQ(kTfLiteFloat32, tensor.type) << "tflite_tensor type is not float";
+
+  const size_t num_output_channels = tensor.dims->data[3];
+  const int dims = 2;
+  const int sizes[] = {tensor.dims->data[1], tensor.dims->data[2]};
+  const int type = CV_MAKETYPE(CV_32F, num_output_channels);
+  return cv::Mat(dims, sizes, type, reinterpret_cast<void*>(tensor.data.f));
+}
+
+cv::Mat ConvertTensorToCvMat(const mediapipe::Tensor& tensor) {
+  // Check tensor is BxCxWxH (size = 4) and the batch size is one(data[0] = 1)
+  CHECK(tensor.shape().dims.size() == 4 && tensor.shape().dims[0] == 1);
+  CHECK_EQ(mediapipe::Tensor::ElementType::kFloat32 == tensor.element_type(),
+           true)
+      << "tensor type is not float";
+
+  const size_t num_output_channels = tensor.shape().dims[3];
+  const int dims = 2;
+  const int sizes[] = {tensor.shape().dims[1], tensor.shape().dims[2]};
+  const int type = CV_MAKETYPE(CV_32F, num_output_channels);
+  auto cpu_view = tensor.GetCpuReadView();
+  return cv::Mat(dims, sizes, type, const_cast<void*>(cpu_view.buffer<void>()));
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/tensor_util.h b/mediapipe/modules/objectron/calculators/tensor_util.h
new file mode 100644
index 0000000..0b26209
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/tensor_util.h
@@ -0,0 +1,31 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TENSOR_UTIL_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TENSOR_UTIL_H_
+
+#include "mediapipe/framework/formats/tensor.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "tensorflow/lite/interpreter.h"
+
+namespace mediapipe {
+
+// Converts a single channel tflite tensor to a grayscale image
+cv::Mat ConvertTfliteTensorToCvMat(const TfLiteTensor& tensor);
+
+// Converts a single channel tensor to grayscale image
+cv::Mat ConvertTensorToCvMat(const mediapipe::Tensor& tensor);
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TENSOR_UTIL_H_
diff --git a/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.cc b/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.cc
new file mode 100644
index 0000000..6989c34
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.cc
@@ -0,0 +1,209 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <memory>
+#include <unordered_map>
+#include <vector>
+
+#include "Eigen/Dense"
+#include "absl/memory/memory.h"
+#include "absl/strings/str_format.h"
+#include "absl/types/span.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/deps/file_path.h"
+#include "mediapipe/framework/formats/tensor.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/belief_decoder_config.pb.h"
+#include "mediapipe/modules/objectron/calculators/decoder.h"
+#include "mediapipe/modules/objectron/calculators/tensor_util.h"
+#include "mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.pb.h"
+
+namespace {
+constexpr char kInputStreamTag[] = "TENSORS";
+constexpr char kOutputStreamTag[] = "ANNOTATIONS";
+
+// Each detection object will be assigned an unique id that starts from 1.
+static int object_id = 0;
+
+inline int GetNextObjectId() { return ++object_id; }
+}  // namespace
+
+namespace mediapipe {
+
+// Convert result Tensors from deep pursuit 3d model into FrameAnnotation.
+//
+// Input:
+//  TENSORS - Vector of Tensor of type kFloat32.
+// Output:
+//  ANNOTATIONS - Result FrameAnnotation.
+//
+// Usage example:
+// node {
+//   calculator: "TensorsToObjectsCalculator"
+//   input_stream: "TENSORS:tensors"
+//   output_stream: "ANNOTATIONS:annotations"
+// }
+class TensorsToObjectsCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
+
+ private:
+  absl::Status ProcessCPU(CalculatorContext* cc,
+                          FrameAnnotation* output_objects);
+  absl::Status LoadOptions(CalculatorContext* cc);
+  // Takes point_3d in FrameAnnotation, projects to 2D, and overwrite the
+  // point_2d field with the projection.
+  void Project3DTo2D(bool portrait, FrameAnnotation* annotation) const;
+  // Increment and assign object ID for each detected object.
+  // In a single MediaPipe session, the IDs are unique.
+  // Also assign timestamp for the FrameAnnotation to be the input packet
+  // timestamp.
+  void AssignObjectIdAndTimestamp(int64 timestamp_us,
+                                  FrameAnnotation* annotation);
+
+  int num_classes_ = 0;
+  int num_keypoints_ = 0;
+
+  ::mediapipe::TensorsToObjectsCalculatorOptions options_;
+  std::unique_ptr<Decoder> decoder_;
+  Eigen::Matrix<float, 4, 4, Eigen::RowMajor> projection_matrix_;
+};
+REGISTER_CALCULATOR(TensorsToObjectsCalculator);
+
+absl::Status TensorsToObjectsCalculator::GetContract(CalculatorContract* cc) {
+  RET_CHECK(!cc->Inputs().GetTags().empty());
+  RET_CHECK(!cc->Outputs().GetTags().empty());
+
+  if (cc->Inputs().HasTag(kInputStreamTag)) {
+    cc->Inputs().Tag(kInputStreamTag).Set<std::vector<mediapipe::Tensor>>();
+  }
+
+  if (cc->Outputs().HasTag(kOutputStreamTag)) {
+    cc->Outputs().Tag(kOutputStreamTag).Set<FrameAnnotation>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status TensorsToObjectsCalculator::Open(CalculatorContext* cc) {
+  MP_RETURN_IF_ERROR(LoadOptions(cc));
+  // clang-format off
+  projection_matrix_ <<
+      1.5731,   0,       0, 0,
+      0,   2.0975,       0, 0,
+      0,        0, -1.0002, -0.2,
+      0,        0,      -1, 0;
+  // clang-format on
+  decoder_ = absl::make_unique<Decoder>(
+      BeliefDecoderConfig(options_.decoder_config()));
+
+  return absl::OkStatus();
+}
+
+absl::Status TensorsToObjectsCalculator::Process(CalculatorContext* cc) {
+  if (cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+
+  auto output_objects = absl::make_unique<FrameAnnotation>();
+
+  MP_RETURN_IF_ERROR(ProcessCPU(cc, output_objects.get()));
+
+  // Output
+  if (cc->Outputs().HasTag(kOutputStreamTag)) {
+    cc->Outputs()
+        .Tag(kOutputStreamTag)
+        .Add(output_objects.release(), cc->InputTimestamp());
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status TensorsToObjectsCalculator::ProcessCPU(
+    CalculatorContext* cc, FrameAnnotation* output_objects) {
+  const auto& input_tensors =
+      cc->Inputs().Tag(kInputStreamTag).Get<std::vector<mediapipe::Tensor>>();
+
+  cv::Mat prediction_heatmap = ConvertTensorToCvMat(input_tensors[0]);
+  cv::Mat offsetmap = ConvertTensorToCvMat(input_tensors[1]);
+
+  *output_objects =
+      decoder_->DecodeBoundingBoxKeypoints(prediction_heatmap, offsetmap);
+  auto status = decoder_->Lift2DTo3D(projection_matrix_, /*portrait*/ true,
+                                     output_objects);
+  if (!status.ok()) {
+    LOG(ERROR) << status;
+    return status;
+  }
+  Project3DTo2D(/*portrait*/ true, output_objects);
+  AssignObjectIdAndTimestamp(cc->InputTimestamp().Microseconds(),
+                             output_objects);
+
+  return absl::OkStatus();
+}
+
+absl::Status TensorsToObjectsCalculator::Close(CalculatorContext* cc) {
+  return absl::OkStatus();
+}
+
+absl::Status TensorsToObjectsCalculator::LoadOptions(CalculatorContext* cc) {
+  // Get calculator options specified in the graph.
+  options_ = cc->Options<::mediapipe::TensorsToObjectsCalculatorOptions>();
+
+  num_classes_ = options_.num_classes();
+  num_keypoints_ = options_.num_keypoints();
+
+  // Currently only support 2D when num_values_per_keypoint equals to 2.
+  CHECK_EQ(options_.num_values_per_keypoint(), 2);
+
+  return absl::OkStatus();
+}
+
+void TensorsToObjectsCalculator::Project3DTo2D(
+    bool portrait, FrameAnnotation* annotation) const {
+  for (auto& ann : *annotation->mutable_annotations()) {
+    for (auto& key_point : *ann.mutable_keypoints()) {
+      Eigen::Vector4f point3d;
+      point3d << key_point.point_3d().x(), key_point.point_3d().y(),
+          key_point.point_3d().z(), 1.0f;
+      Eigen::Vector4f point3d_projection = projection_matrix_ * point3d;
+      float u, v;
+      const float inv_w = 1.0f / point3d_projection(3);
+      if (portrait) {
+        u = (point3d_projection(1) * inv_w + 1.0f) * 0.5f;
+        v = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
+      } else {
+        u = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
+        v = (1.0f - point3d_projection(1) * inv_w) * 0.5f;
+      }
+      key_point.mutable_point_2d()->set_x(u);
+      key_point.mutable_point_2d()->set_y(v);
+    }
+  }
+}
+
+void TensorsToObjectsCalculator::AssignObjectIdAndTimestamp(
+    int64 timestamp_us, FrameAnnotation* annotation) {
+  for (auto& ann : *annotation->mutable_annotations()) {
+    ann.set_object_id(GetNextObjectId());
+  }
+  annotation->set_timestamp(timestamp_us);
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.proto b/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.proto
new file mode 100644
index 0000000..8d46fce
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.proto
@@ -0,0 +1,39 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The option proto for the TensorsToObjectsCalculatorOptions.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+import "mediapipe/modules/objectron/calculators/belief_decoder_config.proto";
+
+message TensorsToObjectsCalculatorOptions {
+  extend CalculatorOptions {
+    optional TensorsToObjectsCalculatorOptions ext = 334361940;
+  }
+
+  // The number of output classes predicted by the detection model.
+  optional int32 num_classes = 1;
+
+  // The number of predicted keypoints.
+  optional int32 num_keypoints = 2;
+  // The dimension of each keypoint, e.g. number of values predicted for each
+  // keypoint.
+  optional int32 num_values_per_keypoint = 3 [default = 2];
+
+  optional BeliefDecoderConfig decoder_config = 4;
+}
diff --git a/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.cc b/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.cc
new file mode 100644
index 0000000..e3686f6
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.cc
@@ -0,0 +1,217 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <memory>
+#include <unordered_map>
+#include <vector>
+
+#include "Eigen/Dense"
+#include "absl/memory/memory.h"
+#include "absl/strings/str_format.h"
+#include "absl/types/span.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/deps/file_path.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
+#include "mediapipe/modules/objectron/calculators/belief_decoder_config.pb.h"
+#include "mediapipe/modules/objectron/calculators/decoder.h"
+#include "mediapipe/modules/objectron/calculators/tensor_util.h"
+#include "mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.pb.h"
+#include "tensorflow/lite/interpreter.h"
+
+namespace {
+constexpr char kInputStreamTag[] = "TENSORS";
+constexpr char kOutputStreamTag[] = "ANNOTATIONS";
+
+// Each detection object will be assigned an unique id that starts from 1.
+static int object_id = 0;
+
+inline int GetNextObjectId() { return ++object_id; }
+}  // namespace
+
+namespace mediapipe {
+
+// Convert result TFLite tensors from deep pursuit 3d model into
+// FrameAnnotation.
+//
+// Input:
+//  TENSORS - Vector of TfLiteTensor of type kTfLiteFloat32.
+// Output:
+//  ANNOTATIONS - Result FrameAnnotation.
+//
+// Usage example:
+// node {
+//   calculator: "TfLiteTensorsToObjectsCalculator"
+//   input_stream: "TENSORS:tensors"
+//   output_stream: "ANNOTATIONS:annotations"
+// }
+class TfLiteTensorsToObjectsCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
+
+ private:
+  absl::Status ProcessCPU(CalculatorContext* cc,
+                          FrameAnnotation* output_objects);
+  absl::Status LoadOptions(CalculatorContext* cc);
+  // Takes point_3d in FrameAnnotation, projects to 2D, and overwrite the
+  // point_2d field with the projection.
+  void Project3DTo2D(bool portrait, FrameAnnotation* annotation) const;
+  // Increment and assign object ID for each detected object.
+  // In a single MediaPipe session, the IDs are unique.
+  // Also assign timestamp for the FrameAnnotation to be the input packet
+  // timestamp.
+  void AssignObjectIdAndTimestamp(int64 timestamp_us,
+                                  FrameAnnotation* annotation);
+
+  int num_classes_ = 0;
+  int num_keypoints_ = 0;
+
+  ::mediapipe::TfLiteTensorsToObjectsCalculatorOptions options_;
+  std::unique_ptr<Decoder> decoder_;
+  Eigen::Matrix<float, 4, 4, Eigen::RowMajor> projection_matrix_;
+};
+REGISTER_CALCULATOR(TfLiteTensorsToObjectsCalculator);
+
+absl::Status TfLiteTensorsToObjectsCalculator::GetContract(
+    CalculatorContract* cc) {
+  RET_CHECK(!cc->Inputs().GetTags().empty());
+  RET_CHECK(!cc->Outputs().GetTags().empty());
+
+  if (cc->Inputs().HasTag(kInputStreamTag)) {
+    cc->Inputs().Tag(kInputStreamTag).Set<std::vector<TfLiteTensor>>();
+  }
+
+  if (cc->Outputs().HasTag(kOutputStreamTag)) {
+    cc->Outputs().Tag(kOutputStreamTag).Set<FrameAnnotation>();
+  }
+  return absl::OkStatus();
+}
+
+absl::Status TfLiteTensorsToObjectsCalculator::Open(CalculatorContext* cc) {
+  MP_RETURN_IF_ERROR(LoadOptions(cc));
+  // Load camera intrinsic matrix.
+  const float fx = options_.normalized_focal_x();
+  const float fy = options_.normalized_focal_y();
+  const float px = options_.normalized_principal_point_x();
+  const float py = options_.normalized_principal_point_y();
+  // clang-format off
+  projection_matrix_ << fx, 0.,  px, 0.,
+                        0., fy,  py, 0.,
+                        0., 0., -1., 0.,
+                        0., 0., -1., 0.;
+  // clang-format on
+  decoder_ = absl::make_unique<Decoder>(
+      BeliefDecoderConfig(options_.decoder_config()));
+
+  return absl::OkStatus();
+}
+
+absl::Status TfLiteTensorsToObjectsCalculator::Process(CalculatorContext* cc) {
+  if (cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
+    return absl::OkStatus();
+  }
+
+  auto output_objects = absl::make_unique<FrameAnnotation>();
+
+  MP_RETURN_IF_ERROR(ProcessCPU(cc, output_objects.get()));
+
+  // Output
+  if (cc->Outputs().HasTag(kOutputStreamTag)) {
+    cc->Outputs()
+        .Tag(kOutputStreamTag)
+        .Add(output_objects.release(), cc->InputTimestamp());
+  }
+
+  return absl::OkStatus();
+}
+
+absl::Status TfLiteTensorsToObjectsCalculator::ProcessCPU(
+    CalculatorContext* cc, FrameAnnotation* output_objects) {
+  const auto& input_tensors =
+      cc->Inputs().Tag(kInputStreamTag).Get<std::vector<TfLiteTensor>>();
+
+  cv::Mat prediction_heatmap = ConvertTfliteTensorToCvMat(input_tensors[0]);
+  cv::Mat offsetmap = ConvertTfliteTensorToCvMat(input_tensors[1]);
+
+  *output_objects =
+      decoder_->DecodeBoundingBoxKeypoints(prediction_heatmap, offsetmap);
+  auto status = decoder_->Lift2DTo3D(projection_matrix_, /*portrait*/ true,
+                                     output_objects);
+  if (!status.ok()) {
+    LOG(ERROR) << status;
+    return status;
+  }
+  Project3DTo2D(/*portrait*/ true, output_objects);
+  AssignObjectIdAndTimestamp(cc->InputTimestamp().Microseconds(),
+                             output_objects);
+
+  return absl::OkStatus();
+}
+
+absl::Status TfLiteTensorsToObjectsCalculator::Close(CalculatorContext* cc) {
+  return absl::OkStatus();
+}
+
+absl::Status TfLiteTensorsToObjectsCalculator::LoadOptions(
+    CalculatorContext* cc) {
+  // Get calculator options specified in the graph.
+  options_ =
+      cc->Options<::mediapipe::TfLiteTensorsToObjectsCalculatorOptions>();
+
+  num_classes_ = options_.num_classes();
+  num_keypoints_ = options_.num_keypoints();
+
+  // Currently only support 2D when num_values_per_keypoint equals to 2.
+  CHECK_EQ(options_.num_values_per_keypoint(), 2);
+
+  return absl::OkStatus();
+}
+
+void TfLiteTensorsToObjectsCalculator::Project3DTo2D(
+    bool portrait, FrameAnnotation* annotation) const {
+  for (auto& ann : *annotation->mutable_annotations()) {
+    for (auto& key_point : *ann.mutable_keypoints()) {
+      Eigen::Vector4f point3d;
+      point3d << key_point.point_3d().x(), key_point.point_3d().y(),
+          key_point.point_3d().z(), 1.0f;
+      Eigen::Vector4f point3d_projection = projection_matrix_ * point3d;
+      float u, v;
+      const float inv_w = 1.0f / point3d_projection(3);
+      if (portrait) {
+        u = (point3d_projection(1) * inv_w + 1.0f) * 0.5f;
+        v = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
+      } else {
+        u = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
+        v = (1.0f - point3d_projection(1) * inv_w) * 0.5f;
+      }
+      key_point.mutable_point_2d()->set_x(u);
+      key_point.mutable_point_2d()->set_y(v);
+    }
+  }
+}
+
+void TfLiteTensorsToObjectsCalculator::AssignObjectIdAndTimestamp(
+    int64 timestamp_us, FrameAnnotation* annotation) {
+  for (auto& ann : *annotation->mutable_annotations()) {
+    ann.set_object_id(GetNextObjectId());
+  }
+  annotation->set_timestamp(timestamp_us);
+}
+
+}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.proto b/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.proto
new file mode 100644
index 0000000..32520d9
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.proto
@@ -0,0 +1,51 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// The option proto for the TfLiteTensorsToObjectsCalculatorOptions.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+import "mediapipe/modules/objectron/calculators/belief_decoder_config.proto";
+
+message TfLiteTensorsToObjectsCalculatorOptions {
+  extend CalculatorOptions {
+    optional TfLiteTensorsToObjectsCalculatorOptions ext = 263667646;
+  }
+
+  // The number of output classes predicted by the detection model.
+  optional int32 num_classes = 1;
+
+  // The number of predicted keypoints.
+  optional int32 num_keypoints = 2;
+  // The dimension of each keypoint, e.g. number of values predicted for each
+  // keypoint.
+  optional int32 num_values_per_keypoint = 3 [default = 2];
+
+  optional BeliefDecoderConfig decoder_config = 4;
+
+  // Camera focal length along x, normalized by width/2.
+  optional float normalized_focal_x = 5 [default = 1.0];
+
+  // Camera focal length along y, normalized by height/2.
+  optional float normalized_focal_y = 6 [default = 1.0];
+
+  // Camera principle point x, normalized by width/2, origin is image center.
+  optional float normalized_principal_point_x = 7 [default = 0.0];
+
+  // Camera principle point y, normalized by height/2, origin is image center.
+  optional float normalized_principal_point_y = 8 [default = 0.0];
+}
diff --git a/mediapipe/modules/objectron/calculators/types.h b/mediapipe/modules/objectron/calculators/types.h
new file mode 100644
index 0000000..dcc477d
--- /dev/null
+++ b/mediapipe/modules/objectron/calculators/types.h
@@ -0,0 +1,56 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TYPES_H_
+#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TYPES_H_
+
+#include <array>
+
+#include "Eigen/Geometry"
+
+namespace mediapipe {
+
+using Eigen::Map;
+using Eigen::Vector2f;
+using Eigen::Vector3f;
+using Eigen::Vector4f;
+using Matrix4f_RM = Eigen::Matrix<float, 4, 4, Eigen::RowMajor>;
+using Matrix3f_RM = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
+
+using Face = std::array<int, 4>;
+
+struct SuperPoint {
+  enum PointSourceType { kPointCloud = 0, kBoundingBox = 1, kSkeleton = 2 };
+  // The id of the point in the point-cloud
+  int reference_point;
+  // The source of the
+  PointSourceType source;
+  // The id of the point in set of points in current frame
+  int id;
+  // If source is kBoundingBox or kSkeleton, object_id stores the id of which \
+  // object this point belongs to.
+  int object_id;
+  // projected u-v value
+  Vector2f uv;
+  Vector2f pixel;
+  // the 3D point
+  Vector3f point_3d;
+  // Color
+  Eigen::Matrix<unsigned char, 4, 1> color;
+  bool rendered;
+};
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TYPES_H_
diff --git a/mediapipe/modules/objectron/object_detection_3d_camera.tflite b/mediapipe/modules/objectron/object_detection_3d_camera.tflite
new file mode 100644
index 0000000..14cb826
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diff --git a/mediapipe/modules/objectron/object_detection_3d_chair.tflite b/mediapipe/modules/objectron/object_detection_3d_chair.tflite
new file mode 100644
index 0000000..3a23dfd
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diff --git a/mediapipe/modules/objectron/object_detection_3d_chair_1stage.tflite b/mediapipe/modules/objectron/object_detection_3d_chair_1stage.tflite
new file mode 100644
index 0000000..718dc97
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diff --git a/mediapipe/modules/objectron/object_detection_3d_cup.tflite b/mediapipe/modules/objectron/object_detection_3d_cup.tflite
new file mode 100644
index 0000000..1a7a5d3
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diff --git a/mediapipe/modules/objectron/object_detection_3d_sneakers.tflite b/mediapipe/modules/objectron/object_detection_3d_sneakers.tflite
new file mode 100644
index 0000000..d64234d
Binary files /dev/null and b/mediapipe/modules/objectron/object_detection_3d_sneakers.tflite differ
diff --git a/mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite b/mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite
new file mode 100644
index 0000000..2077114
Binary files /dev/null and b/mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite differ
diff --git a/mediapipe/modules/objectron/object_detection_oid_v4_cpu.pbtxt b/mediapipe/modules/objectron/object_detection_oid_v4_cpu.pbtxt
new file mode 100644
index 0000000..f7a09fc
--- /dev/null
+++ b/mediapipe/modules/objectron/object_detection_oid_v4_cpu.pbtxt
@@ -0,0 +1,134 @@
+# MediaPipe Objectron object detection CPU subgraph.
+
+type: "ObjectDetectionOidV4Subgraph"
+
+input_stream: "IMAGE:input_video"
+input_side_packet: "LABELS_CSV:allowed_labels"
+output_stream: "DETECTIONS:detections"
+
+# Crops, resizes, and converts the input video into tensor.
+# Preserves aspect ratio of the images.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:input_video"
+  output_stream: "TENSORS:image_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 300
+      output_tensor_height: 300
+      keep_aspect_ratio: false
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite"
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 300
+      input_size_width: 300
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:all_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 24
+      num_boxes: 1917
+      num_coords: 4
+      ignore_classes: 0
+      sigmoid_score: true
+      apply_exponential_on_box_size: true
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "all_detections"
+  output_stream: "labeled_detections"
+  options: {
+    [mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
+      label_map_path: "mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt"
+    }
+  }
+}
+
+# Filters the detections to only those with valid scores
+# for the specified allowed labels.
+node {
+  calculator: "FilterDetectionCalculator"
+  input_stream: "DETECTIONS:labeled_detections"
+  output_stream: "DETECTIONS:filtered_detections"
+  input_side_packet: "LABELS_CSV:allowed_labels"
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.5
+      max_num_detections: 100
+      overlap_type: INTERSECTION_OVER_UNION
+      return_empty_detections: true
+    }
+  }
+}
diff --git a/mediapipe/modules/objectron/object_detection_oid_v4_gpu.pbtxt b/mediapipe/modules/objectron/object_detection_oid_v4_gpu.pbtxt
new file mode 100644
index 0000000..7873e80
--- /dev/null
+++ b/mediapipe/modules/objectron/object_detection_oid_v4_gpu.pbtxt
@@ -0,0 +1,136 @@
+# MediaPipe Objectron object detection GPU subgraph.
+
+type: "ObjectDetectionOidV4Subgraph"
+
+input_stream: "IMAGE_GPU:input_video"
+input_side_packet: "LABELS_CSV:allowed_labels"
+output_stream: "DETECTIONS:detections"
+
+# Crops, resizes, and converts the input video into tensor.
+# Preserves aspect ratio of the images.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "TENSORS:image_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 300
+      output_tensor_height: 300
+      keep_aspect_ratio: false
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:image_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite"
+      delegate { gpu {} }
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 6
+      min_scale: 0.2
+      max_scale: 0.95
+      input_size_height: 300
+      input_size_width: 300
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 16
+      strides: 32
+      strides: 64
+      strides: 128
+      strides: 256
+      strides: 512
+      aspect_ratios: 1.0
+      aspect_ratios: 2.0
+      aspect_ratios: 0.5
+      aspect_ratios: 3.0
+      aspect_ratios: 0.3333
+      reduce_boxes_in_lowest_layer: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:all_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 24
+      num_boxes: 1917
+      num_coords: 4
+      ignore_classes: 0
+      sigmoid_score: true
+      apply_exponential_on_box_size: true
+      x_scale: 10.0
+      y_scale: 10.0
+      h_scale: 5.0
+      w_scale: 5.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Maps detection label IDs to the corresponding label text. The label map is
+# provided in the label_map_path option.
+node {
+  calculator: "DetectionLabelIdToTextCalculator"
+  input_stream: "all_detections"
+  output_stream: "labeled_detections"
+  options: {
+    [mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
+      label_map_path: "object_detection_oidv4_labelmap.txt"
+    }
+  }
+}
+
+# Filters the detections to only those with valid scores
+# for the specified allowed labels.
+node {
+  calculator: "FilterDetectionCalculator"
+  input_stream: "DETECTIONS:labeled_detections"
+  output_stream: "DETECTIONS:filtered_detections"
+  input_side_packet: "LABELS_CSV:allowed_labels"
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "filtered_detections"
+  output_stream: "detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.5
+      max_num_detections: 100
+      overlap_type: INTERSECTION_OVER_UNION
+      return_empty_detections: true
+    }
+  }
+}
diff --git a/mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt b/mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt
new file mode 100644
index 0000000..ef9032c
--- /dev/null
+++ b/mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt
@@ -0,0 +1,24 @@
+???
+Bicycle
+Boot
+Laptop
+Person
+Chair
+Cattle
+Desk
+Cat
+Computer mouse
+Computer monitor
+Box
+Mug
+Coffee cup
+Stationary bicycle
+Table
+Bottle
+High heels
+Vehicle
+Footwear
+Dog
+Book
+Camera
+Car
diff --git a/mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite b/mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite
new file mode 100644
index 0000000..3cb7291
Binary files /dev/null and b/mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite differ
diff --git a/mediapipe/modules/objectron/objectron_cpu.pbtxt b/mediapipe/modules/objectron/objectron_cpu.pbtxt
new file mode 100644
index 0000000..884da05
--- /dev/null
+++ b/mediapipe/modules/objectron/objectron_cpu.pbtxt
@@ -0,0 +1,224 @@
+# MediaPipe Objectron on CPU that produces 3D bounding boxes for objects.
+type: "ObjectronCpuSubgraph"
+
+# Input/Output streams and input side packets.
+input_stream: "IMAGE:image"
+# Path to TfLite model for 3D bounding box landmark prediction
+input_side_packet: "MODEL_PATH:box_landmark_model_path"
+# Allowed category labels, e.g. Footwear, Coffee cup, Mug, Chair, Camera
+input_side_packet: "LABELS_CSV:allowed_labels"
+# Max number of objects to detect/track. (int)
+input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+# Bounding box landmarks topology definition.
+# The numbers are indices in the box_landmarks list.
+#
+#       3 + + + + + + + + 7
+#       +\                +\          UP
+#       + \               + \
+#       +  \              +  \        |
+#       +   4 + + + + + + + + 8       | y
+#       +   +             +   +       |
+#       +   +             +   +       |
+#       +   +     (0)     +   +       .------- x
+#       +   +             +   +        \
+#       1 + + + + + + + + 5   +         \
+#        \  +              \  +          \ z
+#         \ +               \ +           \
+#          \+                \+
+#           2 + + + + + + + + 6
+
+# Collection of detected 3D objects, represented as a FrameAnnotation.
+output_stream: "FRAME_ANNOTATION:detected_objects"
+# Collection of box landmarks. (NormalizedLandmarkList)
+output_stream: "MULTI_LANDMARKS:multi_box_landmarks"
+# Crop rectangles derived from bounding box landmarks.
+output_stream: "NORM_RECTS:multi_box_rects"
+
+# Loads the file in the specified path into a blob.
+node {
+  calculator: "LocalFileContentsCalculator"
+  input_side_packet: "FILE_PATH:0:box_landmark_model_path"
+  output_side_packet: "CONTENTS:0:box_landmark_model_blob"
+}
+
+# Converts the input blob into a TF Lite model.
+node {
+  calculator: "TfLiteModelCalculator"
+  input_side_packet: "MODEL_BLOB:box_landmark_model_blob"
+  output_side_packet: "MODEL:box_landmark_model"
+}
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_box_rects_from_landmarks"
+  output_stream: "gated_prev_box_rects_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided max_num_objects.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:gated_prev_box_rects_from_landmarks"
+  input_side_packet: "max_num_objects"
+  output_stream: "prev_has_enough_objects"
+}
+
+# Drops the incoming image if BoxLandmarkSubgraph was able to identify box
+# presence in the previous image. Otherwise, passes the incoming image through
+# to trigger a new round of box detection in ObjectDetectionOidV4Subgraph.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_objects"
+  output_stream: "detection_image"
+
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Subgraph that performs 2D object detection.
+node {
+  calculator: "ObjectDetectionOidV4Subgraph"
+  input_stream: "IMAGE:detection_image"
+  input_side_packet: "LABELS_CSV:allowed_labels"
+  output_stream: "DETECTIONS:raw_detections"
+}
+
+# Makes sure there are no more detections than provided max_num_objects.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "raw_detections"
+  output_stream: "detections"
+  input_side_packet: "max_num_objects"
+
+}
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts results of box detection into rectangles (normalized by image size)
+# that encloses the box.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTIONS:detections"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECTS:box_rects_from_detections"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      output_zero_rect_for_empty_detections: false
+    }
+  }
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on object detections from the current image. This
+# calculator ensures that the output box_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "box_rects_from_detections"
+  input_stream: "gated_prev_box_rects_from_landmarks"
+  output_stream: "multi_box_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.2
+    }
+  }
+}
+
+# Outputs each element of box_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_box_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:multi_box_rects"
+  input_stream: "CLONE:image"
+  output_stream: "ITEM:single_box_rect"
+  output_stream: "CLONE:landmarks_image"
+  output_stream: "BATCH_END:box_rects_timestamp"
+}
+
+# Subgraph that localizes box landmarks.
+node {
+  calculator: "BoxLandmarkSubgraph"
+  input_stream: "IMAGE:landmarks_image"
+  input_side_packet: "MODEL:box_landmark_model"
+  input_stream: "NORM_RECT:single_box_rect"
+  output_stream: "NORM_LANDMARKS:single_box_landmarks"
+}
+
+# Collects a set of landmarks for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:single_box_landmarks"
+  input_stream: "BATCH_END:box_rects_timestamp"
+  output_stream: "ITERABLE:multi_box_landmarks"
+}
+
+# Convert box landmarks to frame annotations.
+node {
+  calculator: "LandmarksToFrameAnnotationCalculator"
+  input_stream: "MULTI_LANDMARKS:multi_box_landmarks"
+  output_stream: "FRAME_ANNOTATION:box_annotations"
+}
+
+# Lift the 2D landmarks to 3D using EPnP algorithm.
+node {
+  name: "Lift2DFrameAnnotationTo3DCalculator"
+  calculator: "Lift2DFrameAnnotationTo3DCalculator"
+  input_stream: "FRAME_ANNOTATION:box_annotations"
+  output_stream: "LIFTED_FRAME_ANNOTATION:detected_objects"
+  options: {
+    [mediapipe.Lift2DFrameAnnotationTo3DCalculatorOptions.ext] {
+      normalized_focal_x: 1.0
+      normalized_focal_y: 1.0
+    }
+  }
+}
+
+# Get rotated rectangle from detected box.
+node {
+  calculator: "FrameAnnotationToRectCalculator"
+  input_stream: "FRAME_ANNOTATION:detected_objects"
+  output_stream: "NORM_RECTS:box_rects_from_landmarks"
+}
+
+# Caches a box rectangle fed back from boxLandmarkSubgraph, and upon the
+# arrival of the next input image sends out the cached rectangle with the
+# timestamp replaced by that of the input image, essentially generating a packet
+# that carries the previous box rectangle. Note that upon the arrival of the
+# very first input image, an empty packet is sent out to jump start the
+# feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:box_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_box_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt b/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt
new file mode 100644
index 0000000..290b120
--- /dev/null
+++ b/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt
@@ -0,0 +1,83 @@
+# MediaPipe Objectron detection gpu subgraph
+
+type: "ObjectronDetectionSubgraphGpu"
+
+input_stream: "IMAGE_GPU:input_video"
+output_stream: "ANNOTATIONS:objects"
+
+# Transforms the input image on GPU to a 480x640 image. To scale the input
+# image, the scale_mode option is set to FIT to preserve the aspect ratio,
+# resulting in potential letterboxing in the transformed image.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "IMAGE_GPU:transformed_input_video"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 480
+      output_height: 640
+      scale_mode: FIT
+    }
+  }
+}
+
+# Converts the transformed input image on GPU into an image tensor stored as a
+# TfLiteTensor.
+node {
+  calculator: "TfLiteConverterCalculator"
+  input_stream: "IMAGE_GPU:transformed_input_video"
+  output_stream: "TENSORS_GPU:image_tensor"
+}
+
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "opresolver"
+  options: {
+    [mediapipe.TfLiteCustomOpResolverCalculatorOptions.ext] {
+      use_gpu: true
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "TfLiteInferenceCalculator"
+  input_stream: "TENSORS_GPU:image_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
+  options: {
+    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
+      model_path: "object_detection_3d.tflite"
+    }
+  }
+}
+
+# Decodes the model's output tensor (the heatmap and the distance fields) to 2D
+# keypoints. There are nine 2D keypoints: one center keypoint and eight vertices
+# for the 3D bounding box. The calculator parameters determine's the decoder's
+# sensitivity.
+node {
+  calculator: "TfLiteTensorsToObjectsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  output_stream: "ANNOTATIONS:objects"
+  options: {
+    [mediapipe.TfLiteTensorsToObjectsCalculatorOptions.ext] {
+      num_classes: 1
+      num_keypoints: 9
+      decoder_config {
+        heatmap_threshold: 0.6
+        local_max_distance: 2
+        offset_scale_coef: 1.0
+        voting_radius: 2
+        voting_allowance: 1
+        voting_threshold: 0.2
+      }
+      normalized_focal_x: 2.0975
+      normalized_focal_y: 1.5731
+    }
+  }
+}
diff --git a/mediapipe/modules/objectron/objectron_gpu.pbtxt b/mediapipe/modules/objectron/objectron_gpu.pbtxt
new file mode 100644
index 0000000..7ef2b67
--- /dev/null
+++ b/mediapipe/modules/objectron/objectron_gpu.pbtxt
@@ -0,0 +1,186 @@
+# MediaPipe Objectron on GPU that produces 3D bounding boxes for objects.
+type: "ObjectronGpuSubgraph"
+
+# Input/Output streams and input side packets.
+# Note that the input image is assumed to have aspect ratio 3:4 (width:height).
+input_stream: "IMAGE_GPU:image"
+# Allowed category labels, e.g. Footwear, Coffee cup, Mug, Chair, Camera
+input_side_packet: "LABELS_CSV:allowed_labels"
+# Max number of objects to detect/track. (int)
+input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Collection of detected 3D objects, represented as a FrameAnnotation.
+output_stream: "FRAME_ANNOTATION:detected_objects"
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_box_rects_from_landmarks"
+  output_stream: "gated_prev_box_rects_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Determines if an input vector of NormalizedRect has a size greater than or
+# equal to the provided max_num_objects.
+node {
+  calculator: "NormalizedRectVectorHasMinSizeCalculator"
+  input_stream: "ITERABLE:gated_prev_box_rects_from_landmarks"
+  input_side_packet: "max_num_objects"
+  output_stream: "prev_has_enough_objects"
+}
+
+# Drops the incoming image if BoxLandmarkSubgraph was able to identify box
+# presence in the previous image. Otherwise, passes the incoming image through
+# to trigger a new round of box detection in ObjectDetectionOidV4Subgraph.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "DISALLOW:prev_has_enough_objects"
+  output_stream: "detection_image"
+
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Subgraph that performs 2D object detection.
+node {
+  calculator: "ObjectDetectionOidV4Subgraph"
+  input_stream: "IMAGE_GPU:detection_image"
+  input_side_packet: "LABELS_CSV:allowed_labels"
+  output_stream: "DETECTIONS:raw_detections"
+}
+
+# Makes sure there are no more detections than provided max_num_objects.
+node {
+  calculator: "ClipDetectionVectorSizeCalculator"
+  input_stream: "raw_detections"
+  output_stream: "detections"
+  input_side_packet: "max_num_objects"
+
+}
+
+# Extracts image size from the input images.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Converts results of box detection into rectangles (normalized by image size)
+# that encloses the box.
+node {
+  calculator: "DetectionsToRectsCalculator"
+  input_stream: "DETECTIONS:detections"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECTS:box_rects_from_detections"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      output_zero_rect_for_empty_detections: false
+    }
+  }
+}
+
+# Performs association between NormalizedRect vector elements from previous
+# image and rects based on object detections from the current image. This
+# calculator ensures that the output box_rects vector doesn't contain
+# overlapping regions based on the specified min_similarity_threshold.
+node {
+  calculator: "AssociationNormRectCalculator"
+  input_stream: "box_rects_from_detections"
+  input_stream: "gated_prev_box_rects_from_landmarks"
+  output_stream: "box_rects"
+  options: {
+    [mediapipe.AssociationCalculatorOptions.ext] {
+      min_similarity_threshold: 0.2
+    }
+  }
+}
+
+# Outputs each element of box_rects at a fake timestamp for the rest of the
+# graph to process. Clones image and image size packets for each
+# single_box_rect at the fake timestamp. At the end of the loop, outputs the
+# BATCH_END timestamp for downstream calculators to inform them that all
+# elements in the vector have been processed.
+node {
+  calculator: "BeginLoopNormalizedRectCalculator"
+  input_stream: "ITERABLE:box_rects"
+  input_stream: "CLONE:image"
+  output_stream: "ITEM:single_box_rect"
+  output_stream: "CLONE:landmarks_image"
+  output_stream: "BATCH_END:box_rects_timestamp"
+}
+
+# Subgraph that localizes box landmarks.
+node {
+  calculator: "BoxLandmarkSubgraph"
+  input_stream: "IMAGE:landmarks_image"
+  input_stream: "NORM_RECT:single_box_rect"
+  output_stream: "NORM_LANDMARKS:single_box_landmarks"
+}
+
+# Collects a set of landmarks for each hand into a vector. Upon receiving the
+# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
+# timestamp.
+node {
+  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
+  input_stream: "ITEM:single_box_landmarks"
+  input_stream: "BATCH_END:box_rects_timestamp"
+  output_stream: "ITERABLE:multi_box_landmarks"
+}
+
+# Convert box landmarks to frame annotations.
+node {
+  calculator: "LandmarksToFrameAnnotationCalculator"
+  input_stream: "MULTI_LANDMARKS:multi_box_landmarks"
+  output_stream: "FRAME_ANNOTATION:box_annotations"
+}
+
+# Lift the 2D landmarks to 3D using EPnP algorithm.
+node {
+  calculator: "Lift2DFrameAnnotationTo3DCalculator"
+  input_stream: "FRAME_ANNOTATION:box_annotations"
+  output_stream: "LIFTED_FRAME_ANNOTATION:detected_objects"
+  options: {
+    [mediapipe.Lift2DFrameAnnotationTo3DCalculatorOptions.ext] {
+      normalized_focal_x: 2.0975
+      normalized_focal_y: 1.5731
+    }
+  }
+}
+
+# Get rotated rectangle from detected box.
+node {
+  calculator: "FrameAnnotationToRectCalculator"
+  input_stream: "FRAME_ANNOTATION:detected_objects"
+  output_stream: "NORM_RECTS:box_rects_from_landmarks"
+}
+
+# Caches a box rectangle fed back from boxLandmarkSubgraph, and upon the
+# arrival of the next input image sends out the cached rectangle with the
+# timestamp replaced by that of the input image, essentially generating a packet
+# that carries the previous box rectangle. Note that upon the arrival of the
+# very first input image, an empty packet is sent out to jump start the
+# feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:box_rects_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_box_rects_from_landmarks"
+}
diff --git a/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt b/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt
new file mode 100644
index 0000000..eb19a44
--- /dev/null
+++ b/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt
@@ -0,0 +1,176 @@
+# MediaPipe Objectron tracking gpu subgraph
+
+type: "ObjectronTrackingSubgraphGpu"
+
+input_stream: "FRAME_ANNOTATION:objects"
+input_stream: "IMAGE_GPU:input_video"
+output_stream: "LIFTED_FRAME_ANNOTATION:lifted_tracked_objects"
+
+
+# Converts the detected keypoints to Boxes, used by the tracking subgraph.
+node {
+  calculator: "FrameAnnotationToTimedBoxListCalculator"
+  input_stream: "FRAME_ANNOTATION:objects"
+  output_stream: "BOXES:start_pos"
+}
+
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:input_video"
+  output_stream: "IMAGE_GPU:downscaled_input_video"
+  options: {
+    [mediapipe.ImageTransformationCalculatorOptions.ext] {
+      output_width: 240
+      output_height: 320
+    }
+  }
+}
+
+# Converts GPU buffer to ImageFrame for processing tracking.
+node: {
+  calculator: "GpuBufferToImageFrameCalculator"
+  input_stream: "downscaled_input_video"
+  output_stream: "downscaled_input_video_cpu"
+}
+
+# Performs motion analysis on an incoming video stream.
+node: {
+  calculator: "MotionAnalysisCalculator"
+  input_stream: "VIDEO:downscaled_input_video_cpu"
+  output_stream: "CAMERA:camera_motion"
+  output_stream: "FLOW:region_flow"
+
+  options: {
+    [mediapipe.MotionAnalysisCalculatorOptions.ext]: {
+      analysis_options {
+        analysis_policy: ANALYSIS_POLICY_CAMERA_MOBILE
+        flow_options {
+          fast_estimation_min_block_size: 100
+          top_inlier_sets: 1
+          frac_inlier_error_threshold: 3e-3
+          downsample_mode: DOWNSAMPLE_TO_INPUT_SIZE
+          verification_distance: 5.0
+          verify_long_feature_acceleration: true
+          verify_long_feature_trigger_ratio: 0.1
+          tracking_options {
+            max_features: 500
+            adaptive_extraction_levels: 2
+            min_eig_val_settings {
+              adaptive_lowest_quality_level: 2e-4
+            }
+            klt_tracker_implementation: KLT_OPENCV
+          }
+        }
+      }
+    }
+  }
+}
+
+# Reads optical flow fields defined in
+# mediapipe/framework/formats/motion/optical_flow_field.h,
+# returns a VideoFrame with 2 channels (v_x and v_y), each channel is quantized
+# to 0-255.
+node: {
+  calculator: "FlowPackagerCalculator"
+  input_stream: "FLOW:region_flow"
+  input_stream: "CAMERA:camera_motion"
+  output_stream: "TRACKING:tracking_data"
+
+  options: {
+    [mediapipe.FlowPackagerCalculatorOptions.ext]: {
+      flow_packager_options: {
+        binary_tracking_data_support: false
+      }
+    }
+  }
+}
+
+# Tracks box positions over time.
+node: {
+  calculator: "BoxTrackerCalculator"
+  input_stream: "TRACKING:tracking_data"
+  input_stream: "TRACK_TIME:input_video"
+  input_stream: "START_POS:start_pos"
+  input_stream: "CANCEL_OBJECT_ID:cancel_object_id"
+  input_stream_info: {
+    tag_index: "CANCEL_OBJECT_ID"
+    back_edge: true
+  }
+  output_stream: "BOXES:boxes"
+
+  input_stream_handler {
+    input_stream_handler: "SyncSetInputStreamHandler"
+    options {
+      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
+        sync_set {
+          tag_index: "TRACKING"
+          tag_index: "TRACK_TIME"
+        }
+        sync_set {
+          tag_index: "START_POS"
+        }
+        sync_set {
+          tag_index: "CANCEL_OBJECT_ID"
+        }
+      }
+    }
+  }
+
+  options: {
+    [mediapipe.BoxTrackerCalculatorOptions.ext]: {
+      tracker_options: {
+        track_step_options {
+          track_object_and_camera: true
+          tracking_degrees: TRACKING_DEGREE_OBJECT_ROTATION_SCALE
+          inlier_spring_force: 0.0
+          static_motion_temporal_ratio: 3e-2
+        }
+      }
+      visualize_tracking_data: false
+      streaming_track_data_cache_size: 100
+    }
+  }
+}
+
+# Consolidates tracking and detection results.
+node {
+  calculator: "FrameAnnotationTrackerCalculator"
+  input_stream: "FRAME_ANNOTATION:objects"
+  input_stream: "TRACKED_BOXES:boxes"
+  output_stream: "TRACKED_FRAME_ANNOTATION:tracked_objects"
+  output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
+  options: {
+    [mediapipe.FrameAnnotationTrackerCalculatorOptions.ext] {
+      img_width: 240
+      img_height: 320
+      iou_threshold: 0.1
+    }
+  }
+
+  input_stream_handler {
+    input_stream_handler: "SyncSetInputStreamHandler"
+    options {
+      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
+        sync_set {
+          tag_index: "FRAME_ANNOTATION"
+        }
+        sync_set {
+          tag_index: "TRACKED_BOXES"
+        }
+      }
+    }
+  }
+}
+
+# Lift the tracked 2D keypoints to 3D using EPnP algorithm.
+node {
+  calculator: "Lift2DFrameAnnotationTo3DCalculator"
+  input_stream: "FRAME_ANNOTATION:tracked_objects"
+  output_stream: "LIFTED_FRAME_ANNOTATION:lifted_tracked_objects"
+  options: {
+    [mediapipe.Lift2DFrameAnnotationTo3DCalculatorOptions.ext] {
+      normalized_focal_x: 2.0975
+      normalized_focal_y: 1.5731
+    }
+  }
+}
diff --git a/mediapipe/modules/palm_detection/BUILD b/mediapipe/modules/palm_detection/BUILD
new file mode 100644
index 0000000..bed734b
--- /dev/null
+++ b/mediapipe/modules/palm_detection/BUILD
@@ -0,0 +1,71 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+exports_files([
+    "palm_detection_lite.tflite",
+    "palm_detection_full.tflite",
+])
+
+mediapipe_simple_subgraph(
+    name = "palm_detection_model_loader",
+    graph = "palm_detection_model_loader.pbtxt",
+    register_as = "PalmDetectionModelLoader",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/tflite:tflite_model_calculator",
+        "//mediapipe/calculators/util:local_file_contents_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "palm_detection_cpu",
+    graph = "palm_detection_cpu.pbtxt",
+    register_as = "PalmDetectionCpu",
+    deps = [
+        ":palm_detection_model_loader",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "palm_detection_gpu",
+    graph = "palm_detection_gpu.pbtxt",
+    register_as = "PalmDetectionGpu",
+    deps = [
+        ":palm_detection_model_loader",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
diff --git a/mediapipe/modules/palm_detection/README.md b/mediapipe/modules/palm_detection/README.md
new file mode 100644
index 0000000..c7fd610
--- /dev/null
+++ b/mediapipe/modules/palm_detection/README.md
@@ -0,0 +1,7 @@
+# palm_detection
+
+Subgraphs|Details
+:--- | :---
+[`PalmDetectionCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt)| Detects palms/hands. (CPU input.)
+[`PalmDetectionGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt)| Detects palms/hands. (GPU input.)
+
diff --git a/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt b/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt
new file mode 100644
index 0000000..32b3927
--- /dev/null
+++ b/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt
@@ -0,0 +1,147 @@
+# MediaPipe graph to detect palms with TensorFlow Lite on CPU.
+
+type: "PalmDetectionCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Complexity of the palm detection model: 0 or 1. Accuracy as well as inference
+# latency generally go up with the model complexity. If unspecified, functions
+# as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Detected palms. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of palms detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Transforms an image into a 128x128 tensor while keeping the aspect ratio, and
+# therefore may result in potential letterboxing.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:image"
+  output_stream: "TENSORS:input_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 192
+      output_tensor_height: 192
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+    }
+  }
+}
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "opresolver"
+}
+
+# Loads the palm detection TF Lite model.
+node {
+  calculator: "PalmDetectionModelLoader"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
+  input_side_packet: "MODEL:model"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 4
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_width: 192
+      input_size_height: 192
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 8
+      strides: 16
+      strides: 16
+      strides: 16
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:unfiltered_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 2016
+      num_coords: 18
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 7
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+
+      x_scale: 192.0
+      y_scale: 192.0
+      w_scale: 192.0
+      h_scale: 192.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
+# letterboxed image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (the
+# input image to the graph before image transformation).
+node {
+  calculator: "DetectionLetterboxRemovalCalculator"
+  input_stream: "DETECTIONS:filtered_detections"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/palm_detection/palm_detection_full.tflite b/mediapipe/modules/palm_detection/palm_detection_full.tflite
new file mode 100755
index 0000000..aee76a9
Binary files /dev/null and b/mediapipe/modules/palm_detection/palm_detection_full.tflite differ
diff --git a/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt b/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt
new file mode 100644
index 0000000..73e4127
--- /dev/null
+++ b/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt
@@ -0,0 +1,153 @@
+# MediaPipe graph to detect palms with TensorFlow Lite on GPU.
+
+type: "PalmDetectionGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Complexity of the palm detection model: 0 or 1. Accuracy as well as inference
+# latency generally go up with the model complexity. If unspecified, functions
+# as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Detected palms. (std::vector<Detection>)
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of palms detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Transforms an image into a 256x256 tensor while keeping the aspect ratio, and
+# therefore may result in potential letterboxing.
+node {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "TENSORS:input_tensor"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 192
+      output_tensor_height: 192
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "opresolver"
+  options: {
+    [mediapipe.TfLiteCustomOpResolverCalculatorOptions.ext] {
+      use_gpu: true
+    }
+  }
+}
+
+# Loads the palm detection TF Lite model.
+node {
+  calculator: "PalmDetectionModelLoader"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensor"
+  output_stream: "TENSORS:detection_tensors"
+  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
+  input_side_packet: "MODEL:model"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      delegate { gpu {} }
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 4
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_width: 192
+      input_size_height: 192
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 8
+      strides: 16
+      strides: 16
+      strides: 16
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:unfiltered_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 2016
+      num_coords: 18
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 7
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+
+      x_scale: 192.0
+      y_scale: 192.0
+      w_scale: 192.0
+      h_scale: 192.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
+# letterboxed image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (the
+# input image to the graph before image transformation).
+node {
+  calculator: "DetectionLetterboxRemovalCalculator"
+  input_stream: "DETECTIONS:filtered_detections"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/palm_detection/palm_detection_lite.tflite b/mediapipe/modules/palm_detection/palm_detection_lite.tflite
new file mode 100755
index 0000000..a19339a
Binary files /dev/null and b/mediapipe/modules/palm_detection/palm_detection_lite.tflite differ
diff --git a/mediapipe/modules/palm_detection/palm_detection_model_loader.pbtxt b/mediapipe/modules/palm_detection/palm_detection_model_loader.pbtxt
new file mode 100644
index 0000000..f33a76e
--- /dev/null
+++ b/mediapipe/modules/palm_detection/palm_detection_model_loader.pbtxt
@@ -0,0 +1,63 @@
+# MediaPipe graph to load a selected palm detection TF Lite model.
+
+type: "PalmDetectionModelLoader"
+
+# Complexity of the palm detection model: 0 or 1. Accuracy as well as inference
+# latency generally go up with the model complexity. If unspecified, functions
+# as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# TF Lite model represented as a FlatBuffer.
+# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
+output_side_packet: "MODEL:model"
+
+# Determines path to the desired pose landmark model file.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "SELECT:model_complexity"
+  output_side_packet: "PACKET:model_path"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      select: 1
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/palm_detection/palm_detection_lite.tflite"
+            }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/palm_detection/palm_detection_full.tflite"
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Loads the file in the specified path into a blob.
+node {
+  calculator: "LocalFileContentsCalculator"
+  input_side_packet: "FILE_PATH:model_path"
+  output_side_packet: "CONTENTS:model_blob"
+  options: {
+    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
+      text_mode: false
+    }
+  }
+}
+
+# Converts the input blob into a TF Lite model.
+node {
+  calculator: "TfLiteModelCalculator"
+  input_side_packet: "MODEL_BLOB:model_blob"
+  output_side_packet: "MODEL:model"
+}
diff --git a/mediapipe/modules/pose_detection/BUILD b/mediapipe/modules/pose_detection/BUILD
new file mode 100644
index 0000000..f460300
--- /dev/null
+++ b/mediapipe/modules/pose_detection/BUILD
@@ -0,0 +1,56 @@
+# Copyright 2019 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "pose_detection_cpu",
+    graph = "pose_detection_cpu.pbtxt",
+    register_as = "PoseDetectionCpu",
+    deps = [
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_detection_gpu",
+    graph = "pose_detection_gpu.pbtxt",
+    register_as = "PoseDetectionGpu",
+    deps = [
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
+        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
+        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:non_max_suppression_calculator",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "pose_detection.tflite",
+    ],
+)
diff --git a/mediapipe/modules/pose_detection/README.md b/mediapipe/modules/pose_detection/README.md
new file mode 100644
index 0000000..e2e3b2f
--- /dev/null
+++ b/mediapipe/modules/pose_detection/README.md
@@ -0,0 +1,7 @@
+# pose_detection
+
+Subgraphs|Details
+:--- | :---
+[`PoseDetectionCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt)| Detects poses. (CPU input, and inference is executed on CPU.)
+[`PoseDetectionGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt)| Detects poses. (GPU input, and inference is executed on GPU.)
+
diff --git a/mediapipe/modules/pose_detection/pose_detection.tflite b/mediapipe/modules/pose_detection/pose_detection.tflite
new file mode 100755
index 0000000..4f1c521
Binary files /dev/null and b/mediapipe/modules/pose_detection/pose_detection.tflite differ
diff --git a/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt b/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt
new file mode 100644
index 0000000..79ee1ac
--- /dev/null
+++ b/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt
@@ -0,0 +1,159 @@
+# MediaPipe graph to detect poses. (CPU input, and inference is executed on
+# CPU.)
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseDetectionCpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:pose_detections"
+#   }
+
+type: "PoseDetectionCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Detected poses. (std::vector<Detection>)
+# Bounding box in each pose detection is currently set to the bounding box of
+# the detected face. However, 4 additional key points are available in each
+# detection, which are used to further calculate a (rotated) bounding box that
+# encloses the body region of interest. Among the 4 key points, the first two
+# are for identifying the full-body region, and the second two for upper body
+# only:
+#
+# Key point 0 - mid hip center
+# Key point 1 - point that encodes size & rotation (for full body)
+# Key point 2 - mid shoulder center
+# Key point 3 - point that encodes size & rotation (for upper body)
+#
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of poses detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Transforms the input image into a 224x224 one while keeping the aspect ratio
+# (what is expected by the corresponding model), resulting in potential
+# letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 224
+      output_tensor_height: 224
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      # If this calculator truly operates in the CPU, then gpu_origin is
+      # ignored, but if some build switch insists on GPU inference, then we will
+      # still need to set this.
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/pose_detection/pose_detection.tflite"
+      delegate {
+        xnnpack {}
+      }
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 5
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_height: 224
+      input_size_width: 224
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 8
+      strides: 16
+      strides: 32
+      strides: 32
+      strides: 32
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:unfiltered_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 2254
+      num_coords: 12
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 4
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+      x_scale: 224.0
+      y_scale: 224.0
+      h_scale: 224.0
+      w_scale: 224.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
+# letterboxed image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (the
+# input image to the graph before image transformation).
+node {
+  calculator: "DetectionLetterboxRemovalCalculator"
+  input_stream: "DETECTIONS:filtered_detections"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt b/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt
new file mode 100644
index 0000000..b95a117
--- /dev/null
+++ b/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt
@@ -0,0 +1,155 @@
+# MediaPipe graph to detect poses. (GPU input, and inference is executed on
+# GPU.)
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseDetectionGpu"
+#     input_stream: "IMAGE:image"
+#     output_stream: "DETECTIONS:pose_detections"
+#   }
+
+type: "PoseDetectionGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Detected poses. (std::vector<Detection>)
+# Bounding box in each pose detection is currently set to the bounding box of
+# the detected face. However, 4 additional key points are available in each
+# detection, which are used to further calculate a (rotated) bounding box that
+# encloses the body region of interest. Among the 4 key points, the first two
+# are for identifying the full-body region, and the second two for upper body
+# only:
+#
+# Key point 0 - mid hip center
+# Key point 1 - point that encodes size & rotation (for full body)
+# Key point 2 - mid shoulder center
+# Key point 3 - point that encodes size & rotation (for upper body)
+#
+# NOTE: there will not be an output packet in the DETECTIONS stream for this
+# particular timestamp if none of poses detected. However, the MediaPipe
+# framework will internally inform the downstream calculators of the absence of
+# this packet so that they don't wait for it unnecessarily.
+output_stream: "DETECTIONS:detections"
+
+# Transforms the input image into a 224x224 one while keeping the aspect ratio
+# (what is expected by the corresponding model), resulting in potential
+# letterboxing in the transformed image.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 224
+      output_tensor_height: 224
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: -1.0
+        max: 1.0
+      }
+      border_mode: BORDER_ZERO
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
+# vector of tensors representing, for instance, detection boxes/keypoints and
+# scores.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:detection_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      model_path: "mediapipe/modules/pose_detection/pose_detection.tflite"
+      #
+      delegate: { gpu { use_advanced_gpu_api: true } }
+    }
+  }
+}
+
+# Generates a single side packet containing a vector of SSD anchors based on
+# the specification in the options.
+node {
+  calculator: "SsdAnchorsCalculator"
+  output_side_packet: "anchors"
+  options: {
+    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
+      num_layers: 5
+      min_scale: 0.1484375
+      max_scale: 0.75
+      input_size_height: 224
+      input_size_width: 224
+      anchor_offset_x: 0.5
+      anchor_offset_y: 0.5
+      strides: 8
+      strides: 16
+      strides: 32
+      strides: 32
+      strides: 32
+      aspect_ratios: 1.0
+      fixed_anchor_size: true
+    }
+  }
+}
+
+# Decodes the detection tensors generated by the TensorFlow Lite model, based on
+# the SSD anchors and the specification in the options, into a vector of
+# detections. Each detection describes a detected object.
+node {
+  calculator: "TensorsToDetectionsCalculator"
+  input_stream: "TENSORS:detection_tensors"
+  input_side_packet: "ANCHORS:anchors"
+  output_stream: "DETECTIONS:unfiltered_detections"
+  options: {
+    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
+      num_classes: 1
+      num_boxes: 2254
+      num_coords: 12
+      box_coord_offset: 0
+      keypoint_coord_offset: 4
+      num_keypoints: 4
+      num_values_per_keypoint: 2
+      sigmoid_score: true
+      score_clipping_thresh: 100.0
+      reverse_output_order: true
+      x_scale: 224.0
+      y_scale: 224.0
+      h_scale: 224.0
+      w_scale: 224.0
+      min_score_thresh: 0.5
+    }
+  }
+}
+
+# Performs non-max suppression to remove excessive detections.
+node {
+  calculator: "NonMaxSuppressionCalculator"
+  input_stream: "unfiltered_detections"
+  output_stream: "filtered_detections"
+  options: {
+    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
+      min_suppression_threshold: 0.3
+      overlap_type: INTERSECTION_OVER_UNION
+      algorithm: WEIGHTED
+    }
+  }
+}
+
+# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
+# letterboxed image (after image transformation with the FIT scale mode) to the
+# corresponding locations on the same image with the letterbox removed (the
+# input image to the graph before image transformation).
+node {
+  calculator: "DetectionLetterboxRemovalCalculator"
+  input_stream: "DETECTIONS:filtered_detections"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "DETECTIONS:detections"
+}
diff --git a/mediapipe/modules/pose_landmark/BUILD b/mediapipe/modules/pose_landmark/BUILD
new file mode 100644
index 0000000..787f0e2
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/BUILD
@@ -0,0 +1,189 @@
+# Copyright 2020 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_model_loader",
+    graph = "pose_landmark_model_loader.pbtxt",
+    register_as = "PoseLandmarkModelLoader",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/tflite:tflite_model_calculator",
+        "//mediapipe/calculators/util:local_file_contents_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_by_roi_gpu",
+    graph = "pose_landmark_by_roi_gpu.pbtxt",
+    register_as = "PoseLandmarkByRoiGpu",
+    deps = [
+        ":pose_landmark_model_loader",
+        ":pose_landmarks_and_segmentation_inverse_projection",
+        ":tensors_to_pose_landmarks_and_segmentation",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_by_roi_cpu",
+    graph = "pose_landmark_by_roi_cpu.pbtxt",
+    register_as = "PoseLandmarkByRoiCpu",
+    deps = [
+        ":pose_landmark_model_loader",
+        ":pose_landmarks_and_segmentation_inverse_projection",
+        ":tensors_to_pose_landmarks_and_segmentation",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "tensors_to_pose_landmarks_and_segmentation",
+    graph = "tensors_to_pose_landmarks_and_segmentation.pbtxt",
+    register_as = "TensorsToPoseLandmarksAndSegmentation",
+    deps = [
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:split_landmarks_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_segmentation_calculator",
+        "//mediapipe/calculators/util:refine_landmarks_from_heatmap_calculator",
+        "//mediapipe/calculators/util:thresholding_calculator",
+        "//mediapipe/calculators/util:visibility_copy_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmarks_and_segmentation_inverse_projection",
+    graph = "pose_landmarks_and_segmentation_inverse_projection.pbtxt",
+    register_as = "PoseLandmarksAndSegmentationInverseProjection",
+    deps = [
+        "//mediapipe/calculators/image:warp_affine_calculator",
+        "//mediapipe/calculators/util:inverse_matrix_calculator",
+        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
+        "//mediapipe/calculators/util:landmark_projection_calculator",
+        "//mediapipe/calculators/util:world_landmark_projection_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_filtering",
+    graph = "pose_landmark_filtering.pbtxt",
+    register_as = "PoseLandmarkFiltering",
+    deps = [
+        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
+        "//mediapipe/calculators/util:landmarks_smoothing_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:visibility_smoothing_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_segmentation_filtering",
+    graph = "pose_segmentation_filtering.pbtxt",
+    register_as = "PoseSegmentationFiltering",
+    deps = [
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/image:segmentation_smoothing_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_gpu",
+    graph = "pose_landmark_gpu.pbtxt",
+    register_as = "PoseLandmarkGpu",
+    deps = [
+        ":pose_detection_to_roi",
+        ":pose_landmark_by_roi_gpu",
+        ":pose_landmark_filtering",
+        ":pose_landmarks_to_roi",
+        ":pose_segmentation_filtering",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:merge_calculator",
+        "//mediapipe/calculators/core:packet_presence_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+        "//mediapipe/modules/pose_detection:pose_detection_gpu",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmark_cpu",
+    graph = "pose_landmark_cpu.pbtxt",
+    register_as = "PoseLandmarkCpu",
+    deps = [
+        ":pose_detection_to_roi",
+        ":pose_landmark_by_roi_cpu",
+        ":pose_landmark_filtering",
+        ":pose_landmarks_to_roi",
+        ":pose_segmentation_filtering",
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/core:gate_calculator",
+        "//mediapipe/calculators/core:merge_calculator",
+        "//mediapipe/calculators/core:packet_presence_calculator",
+        "//mediapipe/calculators/core:previous_loopback_calculator",
+        "//mediapipe/calculators/core:split_vector_calculator",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+        "//mediapipe/modules/pose_detection:pose_detection_cpu",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "pose_landmark_full.tflite",
+        "pose_landmark_heavy.tflite",
+        "pose_landmark_lite.tflite",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_detection_to_roi",
+    graph = "pose_detection_to_roi.pbtxt",
+    register_as = "PoseDetectionToRoi",
+    deps = [
+        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "pose_landmarks_to_roi",
+    graph = "pose_landmarks_to_roi.pbtxt",
+    register_as = "PoseLandmarksToRoi",
+    deps = [
+        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
+        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
+        "//mediapipe/calculators/util:rect_transformation_calculator",
+    ],
+)
diff --git a/mediapipe/modules/pose_landmark/README.md b/mediapipe/modules/pose_landmark/README.md
new file mode 100644
index 0000000..5752838
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/README.md
@@ -0,0 +1,8 @@
+# pose_landmark
+
+Subgraphs|Details
+:--- | :---
+[`PoseLandmarkByRoiCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (CPU input, and inference is executed on CPU.)
+[`PoseLandmarkByRoiGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (GPU input, and inference is executed on GPU)
+[`PoseLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (CPU input, and inference is executed on CPU)
+[`PoseLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/pose_landmark/pose_detection_to_roi.pbtxt b/mediapipe/modules/pose_landmark/pose_detection_to_roi.pbtxt
new file mode 100644
index 0000000..47f82bb
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_detection_to_roi.pbtxt
@@ -0,0 +1,45 @@
+# MediaPipe graph to calculate pose region of interest (ROI) from a detection
+# provided by "PoseDetectionCpu" or "PoseDetectionGpu"
+#
+# NOTE: this graph is subject to change and should not be used directly.
+
+type: "PoseDetectionToRoi"
+
+# Pose detection. (Detection)
+input_stream: "DETECTION:detection"
+# Frame size (width and height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI according to the first detection of input detections. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts pose detection into a rectangle based on center and scale alignment
+# points.
+node {
+  calculator: "AlignmentPointsRectsCalculator"
+  input_stream: "DETECTION:detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:raw_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0
+      rotation_vector_end_keypoint_index: 1
+      rotation_vector_target_angle_degrees: 90
+    }
+  }
+}
+
+# Expands pose rect with marging used during training.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:raw_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.25
+      scale_y: 1.25
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt
new file mode 100644
index 0000000..b674894
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt
@@ -0,0 +1,178 @@
+# MediaPipe graph to detect/predict pose landmarks and optionally segmentation
+# within an ROI. (CPU input, and inference is executed on CPU.)
+#
+# It is required that "pose_landmark_lite.tflite" or
+# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_COMPLEXITY input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkByRoiCpu"
+#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:roi"
+#     output_stream: "LANDMARKS:landmarks"
+#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
+#   }
+
+type: "PoseLandmarkByRoiCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a pose is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Whether to predict the segmentation mask. If unspecified, functions as set to
+# false. (bool)
+input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+
+# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Pose landmarks within the given ROI. (NormalizedLandmarkList)
+# We have 33 landmarks (see pose_landmark_topology.svg) and there are other
+# auxiliary key points.
+# 0 - nose
+# 1 - left eye (inner)
+# 2 - left eye
+# 3 - left eye (outer)
+# 4 - right eye (inner)
+# 5 - right eye
+# 6 - right eye (outer)
+# 7 - left ear
+# 8 - right ear
+# 9 - mouth (left)
+# 10 - mouth (right)
+# 11 - left shoulder
+# 12 - right shoulder
+# 13 - left elbow
+# 14 - right elbow
+# 15 - left wrist
+# 16 - right wrist
+# 17 - left pinky
+# 18 - right pinky
+# 19 - left index
+# 20 - right index
+# 21 - left thumb
+# 22 - right thumb
+# 23 - left hip
+# 24 - right hip
+# 25 - left knee
+# 26 - right knee
+# 27 - left ankle
+# 28 - right ankle
+# 29 - left heel
+# 30 - right heel
+# 31 - left foot index
+# 32 - right foot index
+#
+# NOTE: If a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:landmarks"
+# Auxiliary landmarks for deriving the ROI in the subsequent image.
+# (NormalizedLandmarkList)
+output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+
+# Pose world landmarks within the given ROI. (LandmarkList)
+# World landmarks are real-world 3D coordinates in meters with the origin at the
+# center between hips. WORLD_LANDMARKS shares the same landmark topology as
+# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
+# projected onto the 2D image surface, while WORLD_LANDMARKS provides
+# coordinates (in meters) of the 3D object itself.
+output_stream: "WORLD_LANDMARKS:world_landmarks"
+
+# Segmentation mask on CPU in ImageFormat::VEC32F1. (Image)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Retrieves the image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Crops and transforms the specified ROI in the input image into an image patch
+# represented as a tensor of dimension expected by the corresponding ML model,
+# while maintaining the aspect ratio of the ROI (which can be different from
+# that of the image patch). Therefore, there can be letterboxing around the ROI
+# in the generated tensor representation.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "MATRIX:transformation_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 256
+      output_tensor_height: 256
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+    }
+  }
+}
+
+# Loads the pose landmark TF Lite model.
+node {
+  calculator: "PoseLandmarkModelLoader"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs model inference on CPU.
+node {
+  calculator: "InferenceCalculator"
+  input_side_packet: "MODEL:model"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:output_tensors"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      delegate { xnnpack {} }
+    }
+  }
+}
+
+# Decodes the tensors into the corresponding landmark and segmentation mask
+# representation.
+node {
+  calculator: "TensorsToPoseLandmarksAndSegmentation"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_stream: "TENSORS:output_tensors"
+  output_stream: "LANDMARKS:roi_landmarks"
+  output_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
+  output_stream: "WORLD_LANDMARKS:roi_world_landmarks"
+  output_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
+}
+
+# Projects the landmarks and segmentation mask in the local coordinates of the
+# (potentially letterboxed) ROI back to the global coordinates of the full input
+# image.
+node {
+  calculator: "PoseLandmarksAndSegmentationInverseProjection"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "NORM_RECT:roi"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  input_stream: "MATRIX:transformation_matrix"
+  input_stream: "LANDMARKS:roi_landmarks"
+  input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
+  input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
+  input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
+  output_stream: "LANDMARKS:landmarks"
+  output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+  output_stream: "WORLD_LANDMARKS:world_landmarks"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt
new file mode 100644
index 0000000..7a2acce
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt
@@ -0,0 +1,174 @@
+# MediaPipe graph to detect/predict pose landmarks and optionally segmentation
+# within an ROI. (GPU input, and inference is executed on GPU.)
+#
+# It is required that "pose_landmark_lite.tflite" or
+# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_COMPLEXITY input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkByRoiGpu"
+#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+#     input_stream: "IMAGE:image"
+#     input_stream: "ROI:roi"
+#     output_stream: "LANDMARKS:landmarks"
+#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
+#   }
+
+type: "PoseLandmarkByRoiGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+# ROI (region of interest) within the given image where a pose is located.
+# (NormalizedRect)
+input_stream: "ROI:roi"
+
+# Whether to predict the segmentation mask. If unspecified, functions as set to
+# false. (bool)
+input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+
+# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Pose landmarks within the given ROI. (NormalizedLandmarkList)
+# We have 33 landmarks (see pose_landmark_topology.svg), and there are other
+# auxiliary key points.
+# 0 - nose
+# 1 - left eye (inner)
+# 2 - left eye
+# 3 - left eye (outer)
+# 4 - right eye (inner)
+# 5 - right eye
+# 6 - right eye (outer)
+# 7 - left ear
+# 8 - right ear
+# 9 - mouth (left)
+# 10 - mouth (right)
+# 11 - left shoulder
+# 12 - right shoulder
+# 13 - left elbow
+# 14 - right elbow
+# 15 - left wrist
+# 16 - right wrist
+# 17 - left pinky
+# 18 - right pinky
+# 19 - left index
+# 20 - right index
+# 21 - left thumb
+# 22 - right thumb
+# 23 - left hip
+# 24 - right hip
+# 25 - left knee
+# 26 - right knee
+# 27 - left ankle
+# 28 - right ankle
+# 29 - left heel
+# 30 - right heel
+# 31 - left foot index
+# 32 - right foot index
+#
+# NOTE: If a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:landmarks"
+# Auxiliary landmarks for deriving the ROI in the subsequent image.
+# (NormalizedLandmarkList)
+output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+
+# Pose world landmarks within the given ROI. (LandmarkList)
+# World landmarks are real-world 3D coordinates in meters with the origin at the
+# center between hips. WORLD_LANDMARKS shares the same landmark topology as
+# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
+# projected onto the 2D image surface, while WORLD_LANDMARKS provides
+# coordinates (in meters) of the 3D object itself.
+output_stream: "WORLD_LANDMARKS:world_landmarks"
+
+# Segmentation mask on GPU in RGBA with the same mask values in R and A. (Image)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Retrieves the image size.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Crops and transforms the specified ROI in the input image into an image patch
+# represented as a tensor of dimension expected by the corresponding ML model,
+# while maintaining the aspect ratio of the ROI (which can be different from
+# that of the image patch). Therefore, there can be letterboxing around the ROI
+# in the generated tensor representation.
+node: {
+  calculator: "ImageToTensorCalculator"
+  input_stream: "IMAGE_GPU:image"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "TENSORS:input_tensors"
+  output_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "MATRIX:transformation_matrix"
+  options: {
+    [mediapipe.ImageToTensorCalculatorOptions.ext] {
+      output_tensor_width: 256
+      output_tensor_height: 256
+      keep_aspect_ratio: true
+      output_tensor_float_range {
+        min: 0.0
+        max: 1.0
+      }
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Loads the pose landmark TF Lite model.
+node {
+  calculator: "PoseLandmarkModelLoader"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs model inference on GPU.
+node {
+  calculator: "InferenceCalculator"
+  input_side_packet: "MODEL:model"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:output_tensors"
+}
+
+# Decodes the tensors into the corresponding landmark and segmentation mask
+# representation.
+node {
+  calculator: "TensorsToPoseLandmarksAndSegmentation"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_stream: "TENSORS:output_tensors"
+  output_stream: "LANDMARKS:roi_landmarks"
+  output_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
+  output_stream: "WORLD_LANDMARKS:roi_world_landmarks"
+  output_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
+}
+
+# Projects the landmarks and segmentation mask in the local coordinates of the
+# (potentially letterboxed) ROI back to the global coordinates of the full input
+# image.
+node {
+  calculator: "PoseLandmarksAndSegmentationInverseProjection"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "NORM_RECT:roi"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  input_stream: "MATRIX:transformation_matrix"
+  input_stream: "LANDMARKS:roi_landmarks"
+  input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
+  input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
+  input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
+  output_stream: "LANDMARKS:landmarks"
+  output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+  output_stream: "WORLD_LANDMARKS:world_landmarks"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt
new file mode 100644
index 0000000..5faf08a
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt
@@ -0,0 +1,268 @@
+# MediaPipe graph to detect/predict pose landmarks. (CPU input, and inference is
+# executed on CPU.) This graph tries to skip pose detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# It is required that "pose_landmark_lite.tflite" or
+# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_COMPLEXITY input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkCpu"
+#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+#     input_stream: "IMAGE:image"
+#     output_stream: "LANDMARKS:pose_landmarks"
+#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
+#   }
+
+type: "PoseLandmarkCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# Whether to filter landmarks across different input images to reduce jitter.
+# If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+
+# Whether to predict the segmentation mask. If unspecified, functions as set to
+# false. (bool)
+input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+
+# Whether to filter segmentation mask across different input images to reduce
+# jitter. If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+
+# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Pose landmarks. (NormalizedLandmarkList)
+# We have 33 landmarks (see pose_landmark_topology.svg), and there are other
+# auxiliary key points.
+# 0 - nose
+# 1 - left eye (inner)
+# 2 - left eye
+# 3 - left eye (outer)
+# 4 - right eye (inner)
+# 5 - right eye
+# 6 - right eye (outer)
+# 7 - left ear
+# 8 - right ear
+# 9 - mouth (left)
+# 10 - mouth (right)
+# 11 - left shoulder
+# 12 - right shoulder
+# 13 - left elbow
+# 14 - right elbow
+# 15 - left wrist
+# 16 - right wrist
+# 17 - left pinky
+# 18 - right pinky
+# 19 - left index
+# 20 - right index
+# 21 - left thumb
+# 22 - right thumb
+# 23 - left hip
+# 24 - right hip
+# 25 - left knee
+# 26 - right knee
+# 27 - left ankle
+# 28 - right ankle
+# 29 - left heel
+# 30 - right heel
+# 31 - left foot index
+# 32 - right foot index
+#
+# NOTE: if a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:pose_landmarks"
+
+# Pose world landmarks. (LandmarkList)
+# World landmarks are real-world 3D coordinates in meters with the origin at the
+# center between hips. WORLD_LANDMARKS shares the same landmark topology as
+# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
+# projected onto the 2D image surface, while WORLD_LANDMARKS provides
+# coordinates (in meters) of the 3D object itself.
+output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
+
+# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Extra outputs (for debugging, for instance).
+# Detected poses. (Detection)
+output_stream: "DETECTION:pose_detection"
+# Regions of interest calculated based on landmarks. (NormalizedRect)
+output_stream: "ROI_FROM_LANDMARKS:pose_rect_from_landmarks"
+# Regions of interest calculated based on pose detections. (NormalizedRect)
+output_stream: "ROI_FROM_DETECTION:pose_rect_from_detection"
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_pose_rect_from_landmarks"
+  output_stream: "gated_prev_pose_rect_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Checks if there's previous pose rect calculated from landmarks.
+node: {
+  calculator: "PacketPresenceCalculator"
+  input_stream: "PACKET:gated_prev_pose_rect_from_landmarks"
+  output_stream: "PRESENCE:prev_pose_rect_from_landmarks_is_present"
+}
+
+# Calculates size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Drops the incoming image if the pose has already been identified from the
+# previous image. Otherwise, passes the incoming image through to trigger a new
+# round of pose detection.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "image_size"
+  input_stream: "DISALLOW:prev_pose_rect_from_landmarks_is_present"
+  output_stream: "image_for_pose_detection"
+  output_stream: "image_size_for_pose_detection"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects poses.
+node {
+  calculator: "PoseDetectionCpu"
+  input_stream: "IMAGE:image_for_pose_detection"
+  output_stream: "DETECTIONS:pose_detections"
+}
+
+# Gets the very first detection from "pose_detections" vector.
+node {
+  calculator: "SplitDetectionVectorCalculator"
+  input_stream: "pose_detections"
+  output_stream: "pose_detection"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Calculates region of interest based on pose detection, so that can be used
+# to detect landmarks.
+node {
+  calculator: "PoseDetectionToRoi"
+  input_stream: "DETECTION:pose_detection"
+  input_stream: "IMAGE_SIZE:image_size_for_pose_detection"
+  output_stream: "ROI:pose_rect_from_detection"
+}
+
+# Selects either pose rect (or ROI) calculated from detection or from previously
+# detected landmarks if available (in this case, calculation of pose rect from
+# detection is skipped).
+node {
+  calculator: "MergeCalculator"
+  input_stream: "pose_rect_from_detection"
+  input_stream: "gated_prev_pose_rect_from_landmarks"
+  output_stream: "pose_rect"
+}
+
+# Detects pose landmarks within specified region of interest of the image.
+node {
+  calculator: "PoseLandmarkByRoiCpu"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:pose_rect"
+  output_stream: "LANDMARKS:unfiltered_pose_landmarks"
+  output_stream: "AUXILIARY_LANDMARKS:unfiltered_auxiliary_landmarks"
+  output_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
+  output_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
+}
+
+# Smoothes landmarks to reduce jitter.
+node {
+  calculator: "PoseLandmarkFiltering"
+  input_side_packet: "ENABLE:smooth_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "NORM_LANDMARKS:unfiltered_pose_landmarks"
+  input_stream: "AUX_NORM_LANDMARKS:unfiltered_auxiliary_landmarks"
+  input_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
+  output_stream: "FILTERED_NORM_LANDMARKS:pose_landmarks"
+  output_stream: "FILTERED_AUX_NORM_LANDMARKS:auxiliary_landmarks"
+  output_stream: "FILTERED_WORLD_LANDMARKS:pose_world_landmarks"
+}
+
+# Calculates region of interest based on the auxiliary landmarks, to be used in
+# the subsequent image.
+node {
+  calculator: "PoseLandmarksToRoi"
+  input_stream: "LANDMARKS:auxiliary_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:pose_rect_from_landmarks"
+}
+
+# Caches pose rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# pose rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:pose_rect_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_pose_rect_from_landmarks"
+}
+
+# Smoothes segmentation to reduce jitter.
+node {
+  calculator: "PoseSegmentationFiltering"
+  input_side_packet: "ENABLE:smooth_segmentation"
+  input_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
+  output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
+}
+
+# Converts the incoming segmentation mask represented as an Image into the
+# corresponding ImageFrame type.
+node: {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:filtered_segmentation_mask"
+  output_stream: "IMAGE_CPU:segmentation_mask"
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_filtering.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_filtering.pbtxt
new file mode 100644
index 0000000..bb3665f
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmark_filtering.pbtxt
@@ -0,0 +1,247 @@
+# MediaPipe graph to filter landmarks temporally (across packets with
+# incremental timestamps) to reduce jitter.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkFiltering"
+#     input_side_packet: "ENABLE:enable"
+#     input_stream: "IMAGE_SIZE:image_size"
+#     input_stream: "NORM_LANDMARKS:landmarks"
+#     input_stream: "AUX_NORM_LANDMARKS:aux_landmarks"
+#     input_stream: "WORLD_LANDMARKS:world_landmarks"
+#     output_stream: "FILTERED_NORM_LANDMARKS:filtered_landmarks"
+#     output_stream: "FILTERED_AUX_NORM_LANDMARKS:filtered_aux_landmarks"
+#     output_stream: "FILTERED_WORLD_LANDMARKS:filtered_world_landmarks"
+#   }
+
+type: "PoseLandmarkFiltering"
+
+# Whether to enable filtering. If unspecified, functions as enabled. (bool)
+input_side_packet: "ENABLE:enable"
+
+# Size of the image (width & height) where the landmarks are estimated from.
+# (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+# Normalized landmarks. (NormalizedLandmarkList)
+input_stream: "NORM_LANDMARKS:landmarks"
+# Auxiliary set of normalized landmarks. (NormalizedLandmarkList)
+input_stream: "AUX_NORM_LANDMARKS:aux_landmarks"
+# World landmarks. (LandmarkList)
+input_stream: "WORLD_LANDMARKS:world_landmarks"
+# Filtered normalized landmarks. (NormalizedLandmarkList)
+output_stream: "FILTERED_NORM_LANDMARKS:filtered_landmarks"
+# Filtered auxiliary set of normalized landmarks. (NormalizedLandmarkList)
+output_stream: "FILTERED_AUX_NORM_LANDMARKS:filtered_aux_landmarks"
+# Filtered world landmarks. (LandmarkList)
+output_stream: "FILTERED_WORLD_LANDMARKS:filtered_world_landmarks"
+
+# Converts landmarks to a detection that tightly encloses all landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:aux_landmarks"
+  output_stream: "DETECTION:aux_detection"
+}
+
+# Converts detection into a rectangle based on center and scale alignment
+# points.
+node {
+  calculator: "AlignmentPointsRectsCalculator"
+  input_stream: "DETECTION:aux_detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0
+      rotation_vector_end_keypoint_index: 1
+      rotation_vector_target_angle_degrees: 90
+    }
+  }
+}
+
+# Smoothes pose landmark visibilities to reduce jitter.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:enable"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  output_stream: "NORM_FILTERED_LANDMARKS:filtered_visibility"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      enable: true
+      contained_node: {
+        calculator: "VisibilitySmoothingCalculator"
+        options: {
+          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
+            no_filter: {}
+          }
+        }
+      }
+      contained_node: {
+        calculator: "VisibilitySmoothingCalculator"
+        options: {
+          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
+            low_pass_filter {
+              alpha: 0.1
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Smoothes pose landmark coordinates to reduce jitter.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:enable"
+  input_stream: "NORM_LANDMARKS:filtered_visibility"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "OBJECT_SCALE_ROI:roi"
+  output_stream: "NORM_FILTERED_LANDMARKS:filtered_landmarks"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      enable: true
+      contained_node: {
+        calculator: "LandmarksSmoothingCalculator"
+        options: {
+          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
+            no_filter: {}
+          }
+        }
+      }
+      contained_node: {
+        calculator: "LandmarksSmoothingCalculator"
+        options: {
+          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
+            one_euro_filter {
+              # Min cutoff 0.1 results into ~0.01 alpha in landmark EMA filter
+              # when landmark is static.
+              min_cutoff: 0.05
+              # Beta 80.0 in combintation with min_cutoff 0.05 results into
+              # ~0.94 alpha in landmark EMA filter when landmark is moving fast.
+              beta: 80.0
+              # Derivative cutoff 1.0 results into ~0.17 alpha in landmark
+              # velocity EMA filter.
+              derivate_cutoff: 1.0
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Smoothes world landmark visibilities to reduce jitter.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:enable"
+  input_stream: "LANDMARKS:world_landmarks"
+  output_stream: "FILTERED_LANDMARKS:filtered_world_visibility"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      enable: true
+      contained_node: {
+        calculator: "VisibilitySmoothingCalculator"
+        options: {
+          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
+            no_filter: {}
+          }
+        }
+      }
+      contained_node: {
+        calculator: "VisibilitySmoothingCalculator"
+        options: {
+          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
+            low_pass_filter {
+              alpha: 0.1
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Smoothes world landmark coordinates to reduce jitter.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "ENABLE:enable"
+  input_stream: "LANDMARKS:filtered_world_visibility"
+  output_stream: "FILTERED_LANDMARKS:filtered_world_landmarks"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      enable: true
+      contained_node: {
+        calculator: "LandmarksSmoothingCalculator"
+        options: {
+          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
+            no_filter: {}
+          }
+        }
+      }
+      contained_node: {
+        calculator: "LandmarksSmoothingCalculator"
+        options: {
+          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
+            one_euro_filter {
+              # Min cutoff 0.1 results into ~ 0.02 alpha in landmark EMA filter
+              # when landmark is static.
+              min_cutoff: 0.1
+              # Beta 40.0 in combintation with min_cutoff 0.1 results into ~0.8
+              # alpha in landmark EMA filter when landmark is moving fast.
+              beta: 40.0
+              # Derivative cutoff 1.0 results into ~0.17 alpha in landmark
+              # velocity EMA filter.
+              derivate_cutoff: 1.0
+              # As world landmdarks are predicted in real world 3D coordintates
+              # in meters (rather than in pixels of input image) prediction
+              # scale does not depend on the pose size in the image.
+              disable_value_scaling: true
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Smoothes auxiliary landmark visibilities to reduce jitter.
+node {
+  calculator: "VisibilitySmoothingCalculator"
+  input_stream: "NORM_LANDMARKS:aux_landmarks"
+  output_stream: "NORM_FILTERED_LANDMARKS:filtered_aux_visibility"
+  options: {
+    [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
+      low_pass_filter {
+        alpha: 0.1
+      }
+    }
+  }
+}
+
+# Smoothes auxiliary landmarks to reduce jitter.
+node {
+  calculator: "LandmarksSmoothingCalculator"
+  input_stream: "NORM_LANDMARKS:filtered_aux_visibility"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "OBJECT_SCALE_ROI:roi"
+  output_stream: "NORM_FILTERED_LANDMARKS:filtered_aux_landmarks"
+  options: {
+    [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
+      # Auxiliary landmarks are smoothed heavier than main landmarks to
+      # make ROI crop for pose landmarks prediction very stable when
+      # object is not moving but responsive enough in case of sudden
+      # movements.
+      one_euro_filter {
+        # Min cutoff 0.01 results into ~0.002 alpha in landmark EMA
+        # filter when landmark is static.
+        min_cutoff: 0.01
+        # Beta 10.0 in combintation with min_cutoff 0.01 results into ~0.68
+        # alpha in landmark EMA filter when landmark is moving fast.
+        beta: 10.0
+        # Derivative cutoff 1.0 results into ~0.17 alpha in landmark
+        # velocity EMA filter.
+        derivate_cutoff: 1.0
+      }
+    }
+  }
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_full.tflite b/mediapipe/modules/pose_landmark/pose_landmark_full.tflite
new file mode 100755
index 0000000..e2ee84f
Binary files /dev/null and b/mediapipe/modules/pose_landmark/pose_landmark_full.tflite differ
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt
new file mode 100644
index 0000000..3ff9ac9
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt
@@ -0,0 +1,268 @@
+# MediaPipe graph to detect/predict pose landmarks. (GPU input, and inference is
+# executed on GPU.) This graph tries to skip pose detection as much as possible
+# by using previously detected/predicted landmarks for new images.
+#
+# It is required that "pose_detection.tflite" is available at
+# "mediapipe/modules/pose_detection/pose_detection.tflite"
+# path during execution.
+#
+# It is required that "pose_landmark_lite.tflite" or
+# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
+# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
+# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_COMPLEXITY input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseLandmarkGpu"
+#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+#     input_stream: "IMAGE:image"
+#     output_stream: "LANDMARKS:pose_landmarks"
+#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
+#   }
+
+type: "PoseLandmarkGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# Whether to filter landmarks across different input images to reduce jitter.
+# If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
+
+# Whether to predict the segmentation mask. If unspecified, functions as set to
+# false. (bool)
+input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+
+# Whether to filter segmentation mask across different input images to reduce
+# jitter. If unspecified, functions as set to true. (bool)
+input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
+
+# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# Whether landmarks on the previous image should be used to help localize
+# landmarks on the current image. (bool)
+input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
+
+# Pose landmarks. (NormalizedLandmarkList)
+# We have 33 landmarks (see pose_landmark_topology.svg), and there are other
+# auxiliary key points.
+# 0 - nose
+# 1 - left eye (inner)
+# 2 - left eye
+# 3 - left eye (outer)
+# 4 - right eye (inner)
+# 5 - right eye
+# 6 - right eye (outer)
+# 7 - left ear
+# 8 - right ear
+# 9 - mouth (left)
+# 10 - mouth (right)
+# 11 - left shoulder
+# 12 - right shoulder
+# 13 - left elbow
+# 14 - right elbow
+# 15 - left wrist
+# 16 - right wrist
+# 17 - left pinky
+# 18 - right pinky
+# 19 - left index
+# 20 - right index
+# 21 - left thumb
+# 22 - right thumb
+# 23 - left hip
+# 24 - right hip
+# 25 - left knee
+# 26 - right knee
+# 27 - left ankle
+# 28 - right ankle
+# 29 - left heel
+# 30 - right heel
+# 31 - left foot index
+# 32 - right foot index
+#
+# NOTE: if a pose is not present within the given ROI, for this particular
+# timestamp there will not be an output packet in the LANDMARKS stream. However,
+# the MediaPipe framework will internally inform the downstream calculators of
+# the absence of this packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:pose_landmarks"
+
+# Pose world landmarks. (LandmarkList)
+# World landmarks are real-world 3D coordinates in meters with the origin at the
+# center between hips. WORLD_LANDMARKS shares the same landmark topology as
+# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
+# projected onto the 2D image surface, while WORLD_LANDMARKS provides
+# coordinates (in meters) of the 3D object itself.
+output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
+
+# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Extra outputs (for debugging, for instance).
+# Detected poses. (Detection)
+output_stream: "DETECTION:pose_detection"
+# Regions of interest calculated based on landmarks. (NormalizedRect)
+output_stream: "ROI_FROM_LANDMARKS:pose_rect_from_landmarks"
+# Regions of interest calculated based on pose detections. (NormalizedRect)
+output_stream: "ROI_FROM_DETECTION:pose_rect_from_detection"
+
+# When the optional input side packet "use_prev_landmarks" is either absent or
+# set to true, uses the landmarks on the previous image to help localize
+# landmarks on the current image.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:use_prev_landmarks"
+  input_stream: "prev_pose_rect_from_landmarks"
+  output_stream: "gated_prev_pose_rect_from_landmarks"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Checks if there's previous pose rect calculated from landmarks.
+node: {
+  calculator: "PacketPresenceCalculator"
+  input_stream: "PACKET:gated_prev_pose_rect_from_landmarks"
+  output_stream: "PRESENCE:prev_pose_rect_from_landmarks_is_present"
+}
+
+# Calculates size of the image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:image_size"
+}
+
+# Drops the incoming image if the pose has already been identified from the
+# previous image. Otherwise, passes the incoming image through to trigger a new
+# round of pose detection.
+node {
+  calculator: "GateCalculator"
+  input_stream: "image"
+  input_stream: "image_size"
+  input_stream: "DISALLOW:prev_pose_rect_from_landmarks_is_present"
+  output_stream: "image_for_pose_detection"
+  output_stream: "image_size_for_pose_detection"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      empty_packets_as_allow: true
+    }
+  }
+}
+
+# Detects poses.
+node {
+  calculator: "PoseDetectionGpu"
+  input_stream: "IMAGE:image_for_pose_detection"
+  output_stream: "DETECTIONS:pose_detections"
+}
+
+# Gets the very first detection from "pose_detections" vector.
+node {
+  calculator: "SplitDetectionVectorCalculator"
+  input_stream: "pose_detections"
+  output_stream: "pose_detection"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      element_only: true
+    }
+  }
+}
+
+# Calculates region of interest based on pose detection, so that can be used
+# to detect landmarks.
+node {
+  calculator: "PoseDetectionToRoi"
+  input_stream: "DETECTION:pose_detection"
+  input_stream: "IMAGE_SIZE:image_size_for_pose_detection"
+  output_stream: "ROI:pose_rect_from_detection"
+}
+
+# Selects either pose rect (or ROI) calculated from detection or from previously
+# detected landmarks if available (in this case, calculation of pose rect from
+# detection is skipped).
+node {
+  calculator: "MergeCalculator"
+  input_stream: "pose_rect_from_detection"
+  input_stream: "gated_prev_pose_rect_from_landmarks"
+  output_stream: "pose_rect"
+}
+
+# Detects pose landmarks within specified region of interest of the image.
+node {
+  calculator: "PoseLandmarkByRoiGpu"
+  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_stream: "IMAGE:image"
+  input_stream: "ROI:pose_rect"
+  output_stream: "LANDMARKS:unfiltered_pose_landmarks"
+  output_stream: "AUXILIARY_LANDMARKS:unfiltered_auxiliary_landmarks"
+  output_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
+  output_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
+}
+
+# Smoothes landmarks to reduce jitter.
+node {
+  calculator: "PoseLandmarkFiltering"
+  input_side_packet: "ENABLE:smooth_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  input_stream: "NORM_LANDMARKS:unfiltered_pose_landmarks"
+  input_stream: "AUX_NORM_LANDMARKS:unfiltered_auxiliary_landmarks"
+  input_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
+  output_stream: "FILTERED_NORM_LANDMARKS:pose_landmarks"
+  output_stream: "FILTERED_AUX_NORM_LANDMARKS:auxiliary_landmarks"
+  output_stream: "FILTERED_WORLD_LANDMARKS:pose_world_landmarks"
+}
+
+# Calculates region of interest based on the auxiliary landmarks, to be used in
+# the subsequent image.
+node {
+  calculator: "PoseLandmarksToRoi"
+  input_stream: "LANDMARKS:auxiliary_landmarks"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "ROI:pose_rect_from_landmarks"
+}
+
+# Caches pose rects calculated from landmarks, and upon the arrival of the next
+# input image, sends out the cached rects with timestamps replaced by that of
+# the input image, essentially generating a packet that carries the previous
+# pose rects. Note that upon the arrival of the very first input image, a
+# timestamp bound update occurs to jump start the feedback loop.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:image"
+  input_stream: "LOOP:pose_rect_from_landmarks"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_pose_rect_from_landmarks"
+}
+
+# Smoothes segmentation to reduce jitter.
+node {
+  calculator: "PoseSegmentationFiltering"
+  input_side_packet: "ENABLE:smooth_segmentation"
+  input_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
+  output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
+}
+
+# Converts the incoming segmentation mask represented as an Image into the
+# corresponding GpuBuffer type.
+node: {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:filtered_segmentation_mask"
+  output_stream: "IMAGE_GPU:segmentation_mask"
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite b/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite
new file mode 100755
index 0000000..9b767e7
Binary files /dev/null and b/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite differ
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite b/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite
new file mode 100755
index 0000000..280cc72
Binary files /dev/null and b/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite differ
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_model_loader.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_model_loader.pbtxt
new file mode 100644
index 0000000..ce7036e
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmark_model_loader.pbtxt
@@ -0,0 +1,73 @@
+# MediaPipe graph to load a selected pose landmark TF Lite model.
+
+type: "PoseLandmarkModelLoader"
+
+# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
+# inference latency generally go up with the model complexity. If unspecified,
+# functions as set to 1. (int)
+input_side_packet: "MODEL_COMPLEXITY:model_complexity"
+
+# TF Lite model represented as a FlatBuffer.
+# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
+output_side_packet: "MODEL:model"
+
+# Determines path to the desired pose landmark model file.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "SELECT:model_complexity"
+  output_side_packet: "PACKET:model_path"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      select: 1
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite"
+            }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/pose_landmark/pose_landmark_full.tflite"
+            }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Loads the file in the specified path into a blob.
+node {
+  calculator: "LocalFileContentsCalculator"
+  input_side_packet: "FILE_PATH:model_path"
+  output_side_packet: "CONTENTS:model_blob"
+  options: {
+    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
+      text_mode: false
+    }
+  }
+}
+
+# Converts the input blob into a TF Lite model.
+node {
+  calculator: "TfLiteModelCalculator"
+  input_side_packet: "MODEL_BLOB:model_blob"
+  output_side_packet: "MODEL:model"
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_topology.svg b/mediapipe/modules/pose_landmark/pose_landmark_topology.svg
new file mode 100644
index 0000000..a57269d
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmark_topology.svg
@@ -0,0 +1,651 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<svg
+   xmlns:dc="http://purl.org/dc/elements/1.1/"
+   xmlns:cc="http://creativecommons.org/ns#"
+   xmlns:svg="http://www.w3.org/2000/svg"
+   xmlns="http://www.w3.org/2000/svg"
+   xmlns:xlink="http://www.w3.org/1999/xlink"
+   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+   version="1.1"
+   id="svg901"
+   width="1000"
+   height="1400"
+   viewBox="0 0 1000 1400"
+   sodipodi:docname="pose_landmark_topology.svg">
+  <defs
+     id="defs905">
+    <marker
+       orient="auto"
+       refY="0"
+       refX="0"
+       id="DotL"
+       style="overflow:visible">
+      <path
+         id="path1015"
+         d="m -2.5,-1 c 0,2.76 -2.24,5 -5,5 -2.76,0 -5,-2.24 -5,-5 0,-2.76 2.24,-5 5,-5 2.76,0 5,2.24 5,5 z"
+         style="fill:#000000;fill-opacity:1;fill-rule:evenodd;stroke:#000000;stroke-width:1pt;stroke-opacity:1"
+         transform="matrix(0.8,0,0,0.8,5.92,0.8)" />
+    </marker>
+  </defs>
+  <sodipodi:namedview
+     pagecolor="#ffffff"
+     bordercolor="#666666"
+     borderopacity="1"
+     objecttolerance="10"
+     gridtolerance="10"
+     guidetolerance="10"
+     id="namedview903"
+     showgrid="false" />
+  <g
+     id="g2654">
+    <path
+       id="path915"
+       d="m 85.266999,515.88093 31.432341,-69.15117 50.29176,67.05568 z"
+       style="fill:none;fill-opacity:1;stroke:#000000;stroke-width:9.07087;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1" />
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diff --git a/mediapipe/modules/pose_landmark/pose_landmarks_and_segmentation_inverse_projection.pbtxt b/mediapipe/modules/pose_landmark/pose_landmarks_and_segmentation_inverse_projection.pbtxt
new file mode 100644
index 0000000..eec3b9b
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmarks_and_segmentation_inverse_projection.pbtxt
@@ -0,0 +1,125 @@
+# MediaPipe graph projecting the landmarks and segmentation mask defined in a
+# local coordinate system within a (potentially letterboxed) ROI back to the
+# global coordinate system of the full image that contains the ROI.
+#
+# EXAMPLE:
+# node {
+#   calculator: "PoseLandmarksAndSegmentationInverseProjection"
+#   input_stream: "IMAGE_SIZE:image_size"
+#   input_stream: "NORM_RECT:roi"
+#   input_stream: "LETTERBOX_PADDING:letterbox_padding"
+#   input_stream: "MATRIX:transformation_matrix"
+#   input_stream: "LANDMARKS:roi_landmarks"
+#   input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
+#   input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
+#   input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
+#   output_stream: "LANDMARKS:landmarks"
+#   output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+#   output_stream: "WORLD_LANDMARKS:world_landmarks"
+#   output_stream: "SEGMENTATION_MASK:segmentation_mask"
+# }
+
+type: "PoseLandmarksAndSegmentationInverseProjection"
+
+# Size of the full image (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+
+# ROI within the full image. (NormalizedRect)
+input_stream: "NORM_RECT:roi"
+
+# An array representing the letterbox padding around the ROI from the 4 sides:
+# [left, top, right, bottom]. The padding is normalized to [0.f, 1.f] by the
+# dimensions of the letterboxed/padded ROI. (std::array<float, 4>)
+input_stream: "LETTERBOX_PADDING:letterbox_padding"
+
+# An array representing a 4x4 row-major-order matrix that maps a point within
+# the ROI from the global coordinates of the full image to the local coordinates
+# within the letterboxed ROI. (std::array<float, 16>)
+input_stream: "MATRIX:transformation_matrix"
+
+# Input landmarks and segmentation mask in local coordinates within the
+# letterboxed ROI, and the corresponding outputs in global coordinates of the
+# full image.
+# LANDMARKS & AUXILIARY_LANDMARKS (NormalizedLandmarkList)
+# WORLD_LANDMARKS (LandmarkList)
+# SEGMENTATION_MASK (Image)
+input_stream: "LANDMARKS:roi_landmarks"
+input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
+input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
+input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
+output_stream: "LANDMARKS:landmarks"
+output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+output_stream: "WORLD_LANDMARKS:world_landmarks"
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# -----------------------------------------------------------------------------
+# LANDMARKS
+# -----------------------------------------------------------------------------
+
+# Adjusts landmarks (already normalized to [0.f, 1.f]) in the letterboxed ROI
+# to the corresponding coordinates with the letterbox removed.
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:roi_landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:adjusted_landmarks"
+}
+node {
+  calculator: "LandmarkLetterboxRemovalCalculator"
+  input_stream: "LANDMARKS:roi_auxiliary_landmarks"
+  input_stream: "LETTERBOX_PADDING:letterbox_padding"
+  output_stream: "LANDMARKS:adjusted_auxiliary_landmarks"
+}
+
+# Projects the landmarks from the letterbox-removed ROI back to the full image.
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:adjusted_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:landmarks"
+}
+node {
+  calculator: "LandmarkProjectionCalculator"
+  input_stream: "NORM_LANDMARKS:adjusted_auxiliary_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "NORM_LANDMARKS:auxiliary_landmarks"
+}
+
+# -----------------------------------------------------------------------------
+# WORLD_LANDMARKS
+# -----------------------------------------------------------------------------
+
+# Projects the world landmarks from the letterboxed ROI to the full image.
+node {
+  calculator: "WorldLandmarkProjectionCalculator"
+  input_stream: "LANDMARKS:roi_world_landmarks"
+  input_stream: "NORM_RECT:roi"
+  output_stream: "LANDMARKS:world_landmarks"
+}
+
+# -----------------------------------------------------------------------------
+# SEGMENTATION_MASK
+# -----------------------------------------------------------------------------
+
+# Calculates the inverse transformation matrix.
+node {
+  calculator: "InverseMatrixCalculator"
+  input_stream: "MATRIX:transformation_matrix"
+  output_stream: "MATRIX:inverse_transformation_matrix"
+}
+
+# Projects the segmentation mask from the letterboxed ROI back to the full
+# image.
+node {
+  calculator: "WarpAffineCalculator"
+  input_stream: "IMAGE:roi_segmentation_mask"
+  input_stream: "MATRIX:inverse_transformation_matrix"
+  input_stream: "OUTPUT_SIZE:image_size"
+  output_stream: "IMAGE:segmentation_mask"
+  options: {
+    [mediapipe.WarpAffineCalculatorOptions.ext] {
+      border_mode: BORDER_ZERO
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
diff --git a/mediapipe/modules/pose_landmark/pose_landmarks_to_roi.pbtxt b/mediapipe/modules/pose_landmark/pose_landmarks_to_roi.pbtxt
new file mode 100644
index 0000000..b1fe0e3
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_landmarks_to_roi.pbtxt
@@ -0,0 +1,51 @@
+# MediaPipe graph to calculate pose region of interest (ROI) from landmarks
+# detected by "PoseLandmarkByRoiCpu" or "PoseLandmarkByRoiGpu".
+#
+# NOTE: this graph is subject to change and should not be used directly.
+
+type: "PoseLandmarksToRoi"
+
+# Normalized landmarks. (NormalizedLandmarkList)
+input_stream: "LANDMARKS:landmarks"
+# Image size (width & height). (std::pair<int, int>)
+input_stream: "IMAGE_SIZE:image_size"
+# ROI according to landmarks. (NormalizedRect)
+output_stream: "ROI:roi"
+
+# Converts landmarks to a detection that tightly encloses all landmarks.
+node {
+  calculator: "LandmarksToDetectionCalculator"
+  input_stream: "NORM_LANDMARKS:landmarks"
+  output_stream: "DETECTION:detection"
+}
+
+# Converts detection into a rectangle based on center and scale alignment
+# points.
+node {
+  calculator: "AlignmentPointsRectsCalculator"
+  input_stream: "DETECTION:detection"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "NORM_RECT:raw_roi"
+  options: {
+    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
+      rotation_vector_start_keypoint_index: 0
+      rotation_vector_end_keypoint_index: 1
+      rotation_vector_target_angle_degrees: 90
+    }
+  }
+}
+
+# Expands pose rect with marging used during training.
+node {
+  calculator: "RectTransformationCalculator"
+  input_stream: "NORM_RECT:raw_roi"
+  input_stream: "IMAGE_SIZE:image_size"
+  output_stream: "roi"
+  options: {
+    [mediapipe.RectTransformationCalculatorOptions.ext] {
+      scale_x: 1.25
+      scale_y: 1.25
+      square_long: true
+    }
+  }
+}
diff --git a/mediapipe/modules/pose_landmark/pose_segmentation_filtering.pbtxt b/mediapipe/modules/pose_landmark/pose_segmentation_filtering.pbtxt
new file mode 100644
index 0000000..c3882ad
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/pose_segmentation_filtering.pbtxt
@@ -0,0 +1,61 @@
+# MediaPipe graph to filter segmentation masks temporally (across packets with
+# incremental timestamps) to reduce jitter.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "PoseSegmentationFiltering"
+#     input_side_packet: "ENABLE:enable"
+#     input_stream: "SEGMENTATION_MASK:segmentation_mask"
+#     output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
+#   }
+
+type: "PoseSegmentationFiltering"
+
+# Whether to enable filtering. If unspecified, functions as enabled. (bool)
+input_side_packet: "ENABLE:enable"
+
+# Segmentation mask. (Image)
+input_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Filtered segmentation mask. (Image)
+output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
+
+# Drops the filtered segmentation mask from the previous frame if filtering is
+# not enabled. In that case, the downstream SegmentationSmoothingCalculator
+# becomes a simple passthrough.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:enable"
+  input_stream: "prev_filtered_segmentation_mask"
+  output_stream: "gated_prev_filtered_segmentation_mask"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: true
+    }
+  }
+}
+
+# Smoothes segmentation to reduce jitter.
+node {
+  calculator: "SegmentationSmoothingCalculator"
+  input_stream: "MASK:segmentation_mask"
+  input_stream: "MASK_PREVIOUS:gated_prev_filtered_segmentation_mask"
+  output_stream: "MASK_SMOOTHED:filtered_segmentation_mask"
+  options {
+    [mediapipe.SegmentationSmoothingCalculatorOptions.ext] {
+      combine_with_previous_ratio: 0.7
+    }
+  }
+}
+
+# Caches the filtered segmentation mask, similar to above for the pose rect.
+node {
+  calculator: "PreviousLoopbackCalculator"
+  input_stream: "MAIN:segmentation_mask"
+  input_stream: "LOOP:filtered_segmentation_mask"
+  input_stream_info: {
+    tag_index: "LOOP"
+    back_edge: true
+  }
+  output_stream: "PREV_LOOP:prev_filtered_segmentation_mask"
+}
diff --git a/mediapipe/modules/pose_landmark/tensors_to_pose_landmarks_and_segmentation.pbtxt b/mediapipe/modules/pose_landmark/tensors_to_pose_landmarks_and_segmentation.pbtxt
new file mode 100644
index 0000000..ac86233
--- /dev/null
+++ b/mediapipe/modules/pose_landmark/tensors_to_pose_landmarks_and_segmentation.pbtxt
@@ -0,0 +1,265 @@
+# MediaPipe graph performing tensor post processing to detect/predict pose
+# landmarks and segmenation mask.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "TensorsToPoseLandmarksAndSegmentation"
+#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+#     input_stream: "TENSORS:tensors"
+#     output_stream: "LANDMARKS:landmarks"
+#     output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+#     output_stream: "WORLD_LANDMARKS:world_landmarks"
+#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
+#   }
+
+type: "TensorsToPoseLandmarksAndSegmentation"
+
+# Whether to predict segmentation mask. If unspecified, functions as set to
+# false. (bool)
+input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+
+# Tensors from mode inference of
+# "mediapipe/modules/pose_landmark/pose_landmark_lite|full|heavy.tflite".
+# (std::vector<Tensor>)
+# tensors[0]: landmarks
+# tensors[1]: pose flag
+# tensors[2]: segmentation
+# tensors[3]: heatmap
+# tensors[4]: world landmarks
+input_stream: "TENSORS:tensors"
+
+# Pose landmarks. (NormalizedLandmarkList)
+# We have 33 landmarks (see pose_landmark_topology.svg) and there are other
+# auxiliary key points.
+# 0 - nose
+# 1 - left eye (inner)
+# 2 - left eye
+# 3 - left eye (outer)
+# 4 - right eye (inner)
+# 5 - right eye
+# 6 - right eye (outer)
+# 7 - left ear
+# 8 - right ear
+# 9 - mouth (left)
+# 10 - mouth (right)
+# 11 - left shoulder
+# 12 - right shoulder
+# 13 - left elbow
+# 14 - right elbow
+# 15 - left wrist
+# 16 - right wrist
+# 17 - left pinky
+# 18 - right pinky
+# 19 - left index
+# 20 - right index
+# 21 - left thumb
+# 22 - right thumb
+# 23 - left hip
+# 24 - right hip
+# 25 - left knee
+# 26 - right knee
+# 27 - left ankle
+# 28 - right ankle
+# 29 - left heel
+# 30 - right heel
+# 31 - left foot index
+# 32 - right foot index
+#
+# NOTE: If a pose is not present, for this particular timestamp there will not
+# be an output packet in the LANDMARKS stream. However, the MediaPipe framework
+# will internally inform the downstream calculators of the absence of this
+# packet so that they don't wait for it unnecessarily.
+output_stream: "LANDMARKS:landmarks"
+# Auxiliary landmarks (e.g., for deriving the ROI in the subsequent image).
+# (NormalizedLandmarkList)
+output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
+
+# Pose world landmarks. (LandmarkList)
+# World landmarks are real-world 3D coordinates in meters with the origin at the
+# center between hips. WORLD_LANDMARKS shares the same landmark topology as
+# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
+# projected onto the 2D image surface, while WORLD_LANDMARKS provides
+# coordinates (in meters) of the 3D object itself.
+output_stream: "WORLD_LANDMARKS:world_landmarks"
+
+# Segmentation mask. (Image)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Splits a vector of tensors to multiple vectors according to the ranges
+# specified in the option.
+node {
+  calculator: "SplitTensorVectorCalculator"
+  input_stream: "tensors"
+  output_stream: "landmark_tensor"
+  output_stream: "pose_flag_tensor"
+  output_stream: "segmentation_tensor"
+  output_stream: "heatmap_tensor"
+  output_stream: "world_landmark_tensor"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 1 }
+      ranges: { begin: 1 end: 2 }
+      ranges: { begin: 2 end: 3 }
+      ranges: { begin: 3 end: 4 }
+      ranges: { begin: 4 end: 5 }
+    }
+  }
+}
+
+# Converts the pose-flag tensor into a float that represents the confidence
+# score of pose presence.
+node {
+  calculator: "TensorsToFloatsCalculator"
+  input_stream: "TENSORS:pose_flag_tensor"
+  output_stream: "FLOAT:pose_presence_score"
+}
+
+# Applies a threshold to the confidence score to determine whether a pose is
+# present.
+node {
+  calculator: "ThresholdingCalculator"
+  input_stream: "FLOAT:pose_presence_score"
+  output_stream: "FLAG:pose_presence"
+  options: {
+    [mediapipe.ThresholdingCalculatorOptions.ext] {
+      threshold: 0.5
+    }
+  }
+}
+
+# Drops input tensors if pose is not present.
+node {
+  calculator: "GateCalculator"
+  input_stream: "landmark_tensor"
+  input_stream: "world_landmark_tensor"
+  input_stream: "segmentation_tensor"
+  input_stream: "heatmap_tensor"
+  input_stream: "ALLOW:pose_presence"
+  output_stream: "ensured_landmark_tensor"
+  output_stream: "ensured_world_landmark_tensor"
+  output_stream: "ensured_segmentation_tensor"
+  output_stream: "ensured_heatmap_tensor"
+}
+
+# -----------------------------------------------------------------------------
+# LANDMARKS
+# -----------------------------------------------------------------------------
+
+# Decodes the landmark tensors into a vector of landmarks, where the landmark
+# coordinates are normalized by the spatial dimensions of the tensor.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_landmark_tensor"
+  output_stream: "NORM_LANDMARKS:raw_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 39
+      input_image_width: 256
+      input_image_height: 256
+      visibility_activation: SIGMOID
+      presence_activation: SIGMOID
+    }
+  }
+}
+
+# Refines landmarks with the heatmap tensor.
+node {
+  calculator: "RefineLandmarksFromHeatmapCalculator"
+  input_stream: "NORM_LANDMARKS:raw_landmarks"
+  input_stream: "TENSORS:ensured_heatmap_tensor"
+  output_stream: "NORM_LANDMARKS:all_landmarks"
+  options: {
+    [mediapipe.RefineLandmarksFromHeatmapCalculatorOptions.ext] {
+      kernel_size: 7
+    }
+  }
+}
+
+# Splits the landmarks into two sets: the actual pose landmarks and the
+# auxiliary landmarks.
+node {
+  calculator: "SplitNormalizedLandmarkListCalculator"
+  input_stream: "all_landmarks"
+  output_stream: "landmarks"
+  output_stream: "auxiliary_landmarks"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 33 }
+      ranges: { begin: 33 end: 35 }
+    }
+  }
+}
+
+# -----------------------------------------------------------------------------
+# WORLD_LANDMARKS
+# -----------------------------------------------------------------------------
+
+# Decodes the world-landmark tensors into a vector of world landmarks.
+node {
+  calculator: "TensorsToLandmarksCalculator"
+  input_stream: "TENSORS:ensured_world_landmark_tensor"
+  output_stream: "LANDMARKS:all_world_landmarks"
+  options: {
+    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
+      num_landmarks: 39
+    }
+  }
+}
+
+# Keeps only the actual world landmarks.
+node {
+  calculator: "SplitLandmarkListCalculator"
+  input_stream: "all_world_landmarks"
+  output_stream: "world_landmarks_without_visibility"
+  options: {
+    [mediapipe.SplitVectorCalculatorOptions.ext] {
+      ranges: { begin: 0 end: 33 }
+    }
+  }
+}
+
+# Reuses the visibility and presence field in pose landmarks for the world
+# landmarks.
+node {
+  calculator: "VisibilityCopyCalculator"
+  input_stream: "NORM_LANDMARKS_FROM:landmarks"
+  input_stream: "LANDMARKS_TO:world_landmarks_without_visibility"
+  output_stream: "LANDMARKS_TO:world_landmarks"
+  options: {
+    [mediapipe.VisibilityCopyCalculatorOptions.ext] {
+      copy_visibility: true
+      copy_presence: true
+    }
+  }
+}
+
+# -----------------------------------------------------------------------------
+# SEGMENTATION_MASK
+# -----------------------------------------------------------------------------
+
+# Drops segmentation tensors if segmentation is not enabled.
+node {
+  calculator: "GateCalculator"
+  input_side_packet: "ALLOW:enable_segmentation"
+  input_stream: "ensured_segmentation_tensor"
+  output_stream: "enabled_segmentation_tensor"
+  options: {
+    [mediapipe.GateCalculatorOptions.ext] {
+      allow: false
+    }
+  }
+}
+
+# Decodes the segmentation tensor into a mask image with pixel values in [0, 1]
+# (1 for person and 0 for background).
+node {
+  calculator: "TensorsToSegmentationCalculator"
+  input_stream: "TENSORS:enabled_segmentation_tensor"
+  output_stream: "MASK:segmentation_mask"
+  options: {
+    [mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
+      activation: SIGMOID
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
diff --git a/mediapipe/modules/selfie_segmentation/BUILD b/mediapipe/modules/selfie_segmentation/BUILD
new file mode 100644
index 0000000..7fc271a
--- /dev/null
+++ b/mediapipe/modules/selfie_segmentation/BUILD
@@ -0,0 +1,99 @@
+# Copyright 2021 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load(
+    "//mediapipe/framework/tool:mediapipe_graph.bzl",
+    "mediapipe_simple_subgraph",
+)
+
+licenses(["notice"])
+
+package(default_visibility = ["//visibility:public"])
+
+mediapipe_simple_subgraph(
+    name = "selfie_segmentation_model_loader",
+    graph = "selfie_segmentation_model_loader.pbtxt",
+    register_as = "SelfieSegmentationModelLoader",
+    deps = [
+        "//mediapipe/calculators/core:constant_side_packet_calculator",
+        "//mediapipe/calculators/tflite:tflite_model_calculator",
+        "//mediapipe/calculators/util:local_file_contents_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "selfie_segmentation_cpu",
+    graph = "selfie_segmentation_cpu.pbtxt",
+    register_as = "SelfieSegmentationCpu",
+    deps = [
+        ":selfie_segmentation_model_loader",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_segmentation_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "selfie_segmentation_gpu",
+    graph = "selfie_segmentation_gpu.pbtxt",
+    register_as = "SelfieSegmentationGpu",
+    deps = [
+        ":selfie_segmentation_model_loader",
+        "//mediapipe/calculators/image:image_properties_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
+        "//mediapipe/calculators/tensor:inference_calculator",
+        "//mediapipe/calculators/tensor:tensors_to_segmentation_calculator",
+        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+        "//mediapipe/framework/tool:switch_container",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "selfie_segmentation_cpu_image",
+    graph = "selfie_segmentation_cpu_image.pbtxt",
+    register_as = "SelfieSegmentationCpuImage",
+    deps = [
+        ":selfie_segmentation_cpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+mediapipe_simple_subgraph(
+    name = "selfie_segmentation_gpu_image",
+    graph = "selfie_segmentation_gpu_image.pbtxt",
+    register_as = "SelfieSegmentationGpuImage",
+    deps = [
+        ":selfie_segmentation_gpu",
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/util:from_image_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+    ],
+)
+
+exports_files(
+    srcs = [
+        "selfie_segmentation.tflite",
+        "selfie_segmentation_landscape.tflite",
+    ],
+)
diff --git a/mediapipe/modules/selfie_segmentation/README.md b/mediapipe/modules/selfie_segmentation/README.md
new file mode 100644
index 0000000..cd6c5e0
--- /dev/null
+++ b/mediapipe/modules/selfie_segmentation/README.md
@@ -0,0 +1,6 @@
+# selfie_segmentation
+
+Subgraphs|Details
+:--- | :---
+[`SelfieSegmentationCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt)| Segments the person from background in a selfie image. (CPU input, and inference is executed on CPU.)
+[`SelfieSegmentationGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt)| Segments the person from background in a selfie image. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite b/mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite
new file mode 100644
index 0000000..374c072
Binary files /dev/null and b/mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite differ
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt
new file mode 100644
index 0000000..5918248
--- /dev/null
+++ b/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt
@@ -0,0 +1,132 @@
+# MediaPipe graph to perform selfie segmentation. (CPU input, and all processing
+# and inference are also performed on CPU)
+#
+# It is required that "selfie_segmentation.tflite" or
+# "selfie_segmentation_landscape.tflite" is available at
+# "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
+# or
+# "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_SELECTION input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "SelfieSegmentationCpu"
+#     input_side_packet: "MODEL_SELECTION:model_selection"
+#     input_stream: "IMAGE:image"
+#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
+#   }
+
+type: "SelfieSegmentationCpu"
+
+# CPU image. (ImageFrame)
+input_stream: "IMAGE:image"
+
+# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
+# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
+# optimized for landscape images. If unspecified, functions as set to 0. (int)
+input_side_packet: "MODEL_SELECTION:model_selection"
+
+# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Resizes the input image into a tensor with a dimension desired by the model.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "SELECT:model_selection"
+  input_stream: "IMAGE:image"
+  output_stream: "TENSORS:input_tensors"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      select: 0
+      contained_node: {
+        calculator: "ImageToTensorCalculator"
+        options: {
+          [mediapipe.ImageToTensorCalculatorOptions.ext] {
+            output_tensor_width: 256
+            output_tensor_height: 256
+            keep_aspect_ratio: false
+            output_tensor_float_range {
+              min: 0.0
+              max: 1.0
+            }
+            border_mode: BORDER_ZERO
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ImageToTensorCalculator"
+        options: {
+          [mediapipe.ImageToTensorCalculatorOptions.ext] {
+            output_tensor_width: 256
+            output_tensor_height: 144
+            keep_aspect_ratio: false
+            output_tensor_float_range {
+              min: 0.0
+              max: 1.0
+            }
+            border_mode: BORDER_ZERO
+          }
+        }
+      }
+    }
+  }
+}
+
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "op_resolver"
+}
+
+# Loads the selfie segmentation TF Lite model.
+node {
+  calculator: "SelfieSegmentationModelLoader"
+  input_side_packet: "MODEL_SELECTION:model_selection"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs model inference on CPU.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:output_tensors"
+  input_side_packet: "MODEL:model"
+  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
+  options: {
+    [mediapipe.InferenceCalculatorOptions.ext] {
+      delegate {
+        xnnpack {}
+      }
+    }
+  }
+}
+
+# Retrieves the size of the input image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_CPU:image"
+  output_stream: "SIZE:input_size"
+}
+
+# Processes the output tensors into a segmentation mask that has the same size
+# as the input image into the graph.
+node {
+  calculator: "TensorsToSegmentationCalculator"
+  input_stream: "TENSORS:output_tensors"
+  input_stream: "OUTPUT_SIZE:input_size"
+  output_stream: "MASK:mask_image"
+  options: {
+    [mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
+      activation: NONE
+    }
+  }
+}
+
+# Converts the incoming Image into the corresponding ImageFrame type.
+node: {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:mask_image"
+  output_stream: "IMAGE_CPU:segmentation_mask"
+}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu_image.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu_image.pbtxt
new file mode 100644
index 0000000..a35ff0e
--- /dev/null
+++ b/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu_image.pbtxt
@@ -0,0 +1,67 @@
+# MediaPipe graph to perform selfie segmentation.
+
+type: "SelfieSegmentationCpuImage"
+
+# Input image. (Image)
+input_stream: "IMAGE:image"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+
+# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
+# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
+# optimized for landscape images. If unspecified, functions as set to 0. (int)
+input_side_packet: "MODEL_SELECTION:model_selection"
+
+# Segmentation mask. (Image)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:segmentation_mask"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Converts Image to ImageFrame for SelfieSegmentationCpu to consume.
+node {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "IMAGE_CPU:raw_image_frame"
+  output_stream: "SOURCE_ON_GPU:is_gpu_image"
+}
+
+# TODO: Remove the extra flipping once adopting MlImage.
+# If the source images are on gpu, flip the data vertically before sending them
+# into SelfieSegmentationCpu. This maybe needed because OpenGL represents images
+# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
+# general assumes the image origin is at the top-left corner.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE:raw_image_frame"
+  input_stream: "FLIP_VERTICALLY:is_gpu_image"
+  output_stream: "IMAGE:image_frame"
+}
+
+node {
+  calculator: "SelfieSegmentationCpu"
+  input_side_packet: "MODEL_SELECTION:model_selection"
+  input_stream: "IMAGE:image_frame"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask_image_frame"
+}
+
+node {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_CPU:segmentation_mask_image_frame"
+  output_stream: "IMAGE:segmentation_mask"
+}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt
new file mode 100644
index 0000000..5f9e55e
--- /dev/null
+++ b/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt
@@ -0,0 +1,133 @@
+# MediaPipe graph to perform selfie segmentation. (GPU input, and all processing
+# and inference are also performed on GPU)
+#
+# It is required that "selfie_segmentation.tflite" or
+# "selfie_segmentation_landscape.tflite" is available at
+# "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
+# or
+# "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
+# path respectively during execution, depending on the specification in the
+# MODEL_SELECTION input side packet.
+#
+# EXAMPLE:
+#   node {
+#     calculator: "SelfieSegmentationGpu"
+#     input_side_packet: "MODEL_SELECTION:model_selection"
+#     input_stream: "IMAGE:image"
+#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
+#   }
+
+type: "SelfieSegmentationGpu"
+
+# GPU image. (GpuBuffer)
+input_stream: "IMAGE:image"
+
+# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
+# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
+# optimized for landscape images. If unspecified, functions as set to 0. (int)
+input_side_packet: "MODEL_SELECTION:model_selection"
+
+# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+# Resizes the input image into a tensor with a dimension desired by the model.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "SELECT:model_selection"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "TENSORS:input_tensors"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      select: 0
+      contained_node: {
+        calculator: "ImageToTensorCalculator"
+        options: {
+          [mediapipe.ImageToTensorCalculatorOptions.ext] {
+            output_tensor_width: 256
+            output_tensor_height: 256
+            keep_aspect_ratio: false
+            output_tensor_float_range {
+              min: 0.0
+              max: 1.0
+            }
+            border_mode: BORDER_ZERO
+            gpu_origin: TOP_LEFT
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ImageToTensorCalculator"
+        options: {
+          [mediapipe.ImageToTensorCalculatorOptions.ext] {
+            output_tensor_width: 256
+            output_tensor_height: 144
+            keep_aspect_ratio: false
+            output_tensor_float_range {
+              min: 0.0
+              max: 1.0
+            }
+            border_mode: BORDER_ZERO
+            gpu_origin: TOP_LEFT
+          }
+        }
+      }
+    }
+  }
+}
+
+# Generates a single side packet containing a TensorFlow Lite op resolver that
+# supports custom ops needed by the model used in this graph.
+node {
+  calculator: "TfLiteCustomOpResolverCalculator"
+  output_side_packet: "op_resolver"
+  options: {
+    [mediapipe.TfLiteCustomOpResolverCalculatorOptions.ext] {
+      use_gpu: true
+    }
+  }
+}
+
+# Loads the selfie segmentation TF Lite model.
+node {
+  calculator: "SelfieSegmentationModelLoader"
+  input_side_packet: "MODEL_SELECTION:model_selection"
+  output_side_packet: "MODEL:model"
+}
+
+# Runs model inference on GPU.
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "TENSORS:input_tensors"
+  output_stream: "TENSORS:output_tensors"
+  input_side_packet: "MODEL:model"
+  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
+}
+
+# Retrieves the size of the input image.
+node {
+  calculator: "ImagePropertiesCalculator"
+  input_stream: "IMAGE_GPU:image"
+  output_stream: "SIZE:input_size"
+}
+
+# Processes the output tensors into a segmentation mask that has the same size
+# as the input image into the graph.
+node {
+  calculator: "TensorsToSegmentationCalculator"
+  input_stream: "TENSORS:output_tensors"
+  input_stream: "OUTPUT_SIZE:input_size"
+  output_stream: "MASK:mask_image"
+  options: {
+    [mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
+      activation: NONE
+      gpu_origin: TOP_LEFT
+    }
+  }
+}
+
+# Converts the incoming Image into the corresponding GpuBuffer type.
+node: {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:mask_image"
+  output_stream: "IMAGE_GPU:segmentation_mask"
+}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu_image.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu_image.pbtxt
new file mode 100644
index 0000000..d5c0935
--- /dev/null
+++ b/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu_image.pbtxt
@@ -0,0 +1,67 @@
+# MediaPipe graph to perform selfie segmentation.
+
+type: "SelfieSegmentationGpuImage"
+
+# Input image. (Image)
+input_stream: "IMAGE:image"
+
+# The throttled input image. (Image)
+output_stream: "IMAGE:throttled_image"
+
+# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
+# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
+# optimized for landscape images. If unspecified, functions as set to 0. (int)
+input_side_packet: "MODEL_SELECTION:model_selection"
+
+# Segmentation mask. (Image)
+output_stream: "SEGMENTATION_MASK:segmentation_mask"
+
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "image"
+  input_stream: "FINISHED:segmentation_mask"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_image"
+  options: {
+    [mediapipe.FlowLimiterCalculatorOptions.ext] {
+      max_in_flight: 1
+      max_in_queue: 1
+    }
+  }
+}
+
+# Converts Image to ImageFrame for SelfieSegmentationGpu to consume.
+node {
+  calculator: "FromImageCalculator"
+  input_stream: "IMAGE:throttled_image"
+  output_stream: "IMAGE_GPU:raw_gpu_buffer"
+  output_stream: "SOURCE_ON_GPU:is_gpu_image"
+}
+
+# TODO: Remove the extra flipping once adopting MlImage.
+# If the source images are on gpu, flip the data vertically before sending them
+# into SelfieSegmentationGpu. This maybe needed because OpenGL represents images
+# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
+# general assumes the image origin is at the top-left corner.
+node: {
+  calculator: "ImageTransformationCalculator"
+  input_stream: "IMAGE_GPU:raw_gpu_buffer"
+  input_stream: "FLIP_VERTICALLY:is_gpu_image"
+  output_stream: "IMAGE_GPU:gpu_buffer"
+}
+
+node {
+  calculator: "SelfieSegmentationGpu"
+  input_side_packet: "MODEL_SELECTION:model_selection"
+  input_stream: "IMAGE:gpu_buffer"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask_gpu_buffer"
+}
+
+node {
+  calculator: "ToImageCalculator"
+  input_stream: "IMAGE_GPU:segmentation_mask_gpu_buffer"
+  output_stream: "IMAGE:segmentation_mask"
+}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite b/mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite
new file mode 100755
index 0000000..4ea3f8a
Binary files /dev/null and b/mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite differ
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_model_loader.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_model_loader.pbtxt
new file mode 100644
index 0000000..39495f8
--- /dev/null
+++ b/mediapipe/modules/selfie_segmentation/selfie_segmentation_model_loader.pbtxt
@@ -0,0 +1,63 @@
+# MediaPipe graph to load a selected selfie segmentation TF Lite model.
+
+type: "SelfieSegmentationModelLoader"
+
+# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
+# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
+# optimized for landscape images. If unspecified, functions as set to 0. (int)
+input_side_packet: "MODEL_SELECTION:model_selection"
+
+# TF Lite model represented as a FlatBuffer.
+# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
+output_side_packet: "MODEL:model"
+
+# Determines path to the desired pose landmark model file.
+node {
+  calculator: "SwitchContainer"
+  input_side_packet: "SELECT:model_selection"
+  output_side_packet: "PACKET:model_path"
+  options: {
+    [mediapipe.SwitchContainerOptions.ext] {
+      select: 0
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
+            }
+          }
+        }
+      }
+      contained_node: {
+        calculator: "ConstantSidePacketCalculator"
+        options: {
+          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
+            packet {
+              string_value: "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+# Loads the file in the specified path into a blob.
+node {
+  calculator: "LocalFileContentsCalculator"
+  input_side_packet: "FILE_PATH:model_path"
+  output_side_packet: "CONTENTS:model_blob"
+  options: {
+    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
+      text_mode: false
+    }
+  }
+}
+
+# Converts the input blob into a TF Lite model.
+node {
+  calculator: "TfLiteModelCalculator"
+  input_side_packet: "MODEL_BLOB:model_blob"
+  output_side_packet: "MODEL:model"
+}
diff --git a/src/bindings.rs b/src/bindings.rs
new file mode 100644
index 0000000..0914443
--- /dev/null
+++ b/src/bindings.rs
@@ -0,0 +1,4226 @@
+/* automatically generated by rust-bindgen 0.59.2 */
+
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct std_allocator {
+    pub _address: u8,
+}
+pub type std_allocator_value_type = u8;
+pub type std_allocator_size_type = u64;
+pub type std_allocator_difference_type = u64;
+pub type std_allocator_pointer = u8;
+pub type std_allocator_const_pointer = u8;
+pub type std_allocator_reference = u8;
+pub type std_allocator_const_reference = u8;
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct std_allocator_rebind {
+    pub _address: u8,
+}
+pub type std_allocator_rebind_other = u8;
+pub type std_allocator_propagate_on_container_move_assignment = u8;
+pub type std_allocator_is_always_equal = u8;
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct std_vector {
+    pub _address: u8,
+}
+pub type std_vector__Base = u8;
+pub type std_vector__Tp_alloc_type = u8;
+pub type std_vector__Alloc_traits = u8;
+pub type std_vector_value_type = u8;
+pub type std_vector_pointer = u8;
+pub type std_vector_const_pointer = u8;
+pub type std_vector_reference = u8;
+pub type std_vector_const_reference = u8;
+pub type std_vector_iterator = u8;
+pub type std_vector_const_iterator = u8;
+pub type std_vector_const_reverse_iterator = u8;
+pub type std_vector_reverse_iterator = u8;
+pub type std_vector_size_type = u64;
+pub type std_vector_difference_type = u64;
+pub type std_vector_allocator_type = u8;
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct std_vector__Temporary_value {
+    pub _address: u8,
+}
+pub type size_t = ::std::os::raw::c_ulong;
+pub type uchar = ::std::os::raw::c_uchar;
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_cuda_GpuMat {
+    _unused: [u8; 0],
+}
+#[doc = " @brief Template class for 2D points specified by its coordinates `x` and `y`."]
+#[doc = ""]
+#[doc = "An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is"]
+#[doc = "also a cast operator to convert point coordinates to the specified type. The conversion from"]
+#[doc = "floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion"]
+#[doc = "uses this operation for each of the coordinates. Besides the class members listed in the"]
+#[doc = "declaration above, the following operations on points are implemented:"]
+#[doc = "@code"]
+#[doc = "pt1 = pt2 + pt3;"]
+#[doc = "pt1 = pt2 - pt3;"]
+#[doc = "pt1 = pt2 * a;"]
+#[doc = "pt1 = a * pt2;"]
+#[doc = "pt1 = pt2 / a;"]
+#[doc = "pt1 += pt2;"]
+#[doc = "pt1 -= pt2;"]
+#[doc = "pt1 *= a;"]
+#[doc = "pt1 /= a;"]
+#[doc = "double value = norm(pt); // L2 norm"]
+#[doc = "pt1 == pt2;"]
+#[doc = "pt1 != pt2;"]
+#[doc = "@endcode"]
+#[doc = "For your convenience, the following type aliases are defined:"]
+#[doc = "@code"]
+#[doc = "typedef Point_<int> Point2i;"]
+#[doc = "typedef Point2i Point;"]
+#[doc = "typedef Point_<float> Point2f;"]
+#[doc = "typedef Point_<double> Point2d;"]
+#[doc = "@endcode"]
+#[doc = "Example:"]
+#[doc = "@code"]
+#[doc = "Point2f a(0.3f, 0.f), b(0.f, 0.4f);"]
+#[doc = "Point pt = (a + b)*10.f;"]
+#[doc = "cout << pt.x << \", \" << pt.y << endl;"]
+#[doc = "@endcode"]
+pub type cv_Point = [u32; 2usize];
+#[doc = " @brief Template class for specifying the size of an image or rectangle."]
+#[doc = ""]
+#[doc = "The class includes two members called width and height. The structure can be converted to and from"]
+#[doc = "the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison"]
+#[doc = "operations as for Point_ is available."]
+#[doc = ""]
+#[doc = "OpenCV defines the following Size_\\<\\> aliases:"]
+#[doc = "@code"]
+#[doc = "typedef Size_<int> Size2i;"]
+#[doc = "typedef Size2i Size;"]
+#[doc = "typedef Size_<float> Size2f;"]
+#[doc = "@endcode"]
+pub type cv_Size = [u32; 2usize];
+#[doc = " @brief Template class for 2D rectangles"]
+#[doc = ""]
+#[doc = "described by the following parameters:"]
+#[doc = "-   Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y"]
+#[doc = "in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner."]
+#[doc = "-   Rectangle width and height."]
+#[doc = ""]
+#[doc = "OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the"]
+#[doc = "right and bottom boundaries are not. For example, the method Rect_::contains returns true if"]
+#[doc = ""]
+#[doc = "\\f[x  \\leq pt.x < x+width,"]
+#[doc = "y  \\leq pt.y < y+height\\f]"]
+#[doc = ""]
+#[doc = "Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\\<int\\> ) is"]
+#[doc = "implemented as:"]
+#[doc = "@code"]
+#[doc = "for(int y = roi.y; y < roi.y + roi.height; y++)"]
+#[doc = "for(int x = roi.x; x < roi.x + roi.width; x++)"]
+#[doc = "{"]
+#[doc = "}"]
+#[doc = "@endcode"]
+#[doc = "In addition to the class members, the following operations on rectangles are implemented:"]
+#[doc = "-   \\f$\\texttt{rect} = \\texttt{rect} \\pm \\texttt{point}\\f$ (shifting a rectangle by a certain offset)"]
+#[doc = "-   \\f$\\texttt{rect} = \\texttt{rect} \\pm \\texttt{size}\\f$ (expanding or shrinking a rectangle by a"]
+#[doc = "certain amount)"]
+#[doc = "-   rect += point, rect -= point, rect += size, rect -= size (augmenting operations)"]
+#[doc = "-   rect = rect1 & rect2 (rectangle intersection)"]
+#[doc = "-   rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 )"]
+#[doc = "-   rect &= rect1, rect |= rect1 (and the corresponding augmenting operations)"]
+#[doc = "-   rect == rect1, rect != rect1 (rectangle comparison)"]
+#[doc = ""]
+#[doc = "This is an example how the partial ordering on rectangles can be established (rect1 \\f$\\subseteq\\f$"]
+#[doc = "rect2):"]
+#[doc = "@code"]
+#[doc = "template<typename _Tp> inline bool"]
+#[doc = "operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2)"]
+#[doc = "{"]
+#[doc = "return (r1 & r2) == r1;"]
+#[doc = "}"]
+#[doc = "@endcode"]
+#[doc = "For your convenience, the Rect_\\<\\> alias is available: cv::Rect"]
+pub type cv_Rect = [u32; 4usize];
+#[doc = " @brief Template class specifying a continuous subsequence (slice) of a sequence."]
+#[doc = ""]
+#[doc = "The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes."]
+#[doc = "Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an"]
+#[doc = "inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a"]
+#[doc = "half-opened interval is usually denoted as \\f$[start,end)\\f$ ."]
+#[doc = ""]
+#[doc = "The static method Range::all() returns a special variable that means \"the whole sequence\" or \"the"]
+#[doc = "whole range\", just like \" : \" in Matlab or \" ... \" in Python. All the methods and functions in"]
+#[doc = "OpenCV that take Range support this special Range::all() value. But, of course, in case of your own"]
+#[doc = "custom processing, you will probably have to check and handle it explicitly:"]
+#[doc = "@code"]
+#[doc = "void my_function(..., const Range& r, ....)"]
+#[doc = "{"]
+#[doc = "if(r == Range::all()) {"]
+#[doc = "}"]
+#[doc = "else {"]
+#[doc = "}"]
+#[doc = "}"]
+#[doc = "@endcode"]
+#[repr(C)]
+#[repr(align(4))]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_Range {
+    pub _bindgen_opaque_blob: [u32; 2usize],
+}
+#[test]
+fn bindgen_test_layout_cv_Range() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_Range>(),
+        8usize,
+        concat!("Size of: ", stringify!(cv_Range))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_Range>(),
+        4usize,
+        concat!("Alignment of ", stringify!(cv_Range))
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5Range4sizeEv"]
+    pub fn cv_Range_size(this: *const cv_Range) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5Range5emptyEv"]
+    pub fn cv_Range_empty(this: *const cv_Range) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv5Range3allEv"]
+    pub fn cv_Range_all() -> cv_Range;
+}
+extern "C" {
+    #[doc = " Range /////////////////////////////////"]
+    #[link_name = "\u{1}_ZN2cv5RangeC1Ev"]
+    pub fn cv_Range_Range(this: *mut cv_Range);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv5RangeC1Eii"]
+    pub fn cv_Range_Range1(
+        this: *mut cv_Range,
+        _start: ::std::os::raw::c_int,
+        _end: ::std::os::raw::c_int,
+    );
+}
+impl cv_Range {
+    #[inline]
+    pub unsafe fn size(&self) -> ::std::os::raw::c_int {
+        cv_Range_size(self)
+    }
+    #[inline]
+    pub unsafe fn empty(&self) -> bool {
+        cv_Range_empty(self)
+    }
+    #[inline]
+    pub unsafe fn all() -> cv_Range {
+        cv_Range_all()
+    }
+    #[inline]
+    pub unsafe fn new() -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Range_Range(__bindgen_tmp.as_mut_ptr());
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new1(_start: ::std::os::raw::c_int, _end: ::std::os::raw::c_int) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Range_Range1(__bindgen_tmp.as_mut_ptr(), _start, _end);
+        __bindgen_tmp.assume_init()
+    }
+}
+#[doc = " @brief Template class for a 4-element vector derived from Vec."]
+#[doc = ""]
+#[doc = "Being derived from Vec\\<_Tp, 4\\> , Scalar\\_ and Scalar can be used just as typical 4-element"]
+#[doc = "vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in"]
+#[doc = "OpenCV to pass pixel values."]
+pub type cv_Scalar = [u64; 4usize];
+#[doc = "! @addtogroup core"]
+#[doc = "! @{"]
+#[repr(C)]
+#[repr(align(8))]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_BufferPoolController {
+    pub _bindgen_opaque_blob: u64,
+}
+#[test]
+fn bindgen_test_layout_cv_BufferPoolController() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_BufferPoolController>(),
+        8usize,
+        concat!("Size of: ", stringify!(cv_BufferPoolController))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_BufferPoolController>(),
+        8usize,
+        concat!("Alignment of ", stringify!(cv_BufferPoolController))
+    );
+}
+pub const cv_AccessFlag_ACCESS_READ: cv_AccessFlag = 16777216;
+pub const cv_AccessFlag_ACCESS_WRITE: cv_AccessFlag = 33554432;
+pub const cv_AccessFlag_ACCESS_RW: cv_AccessFlag = 50331648;
+pub const cv_AccessFlag_ACCESS_MASK: cv_AccessFlag = 50331648;
+pub const cv_AccessFlag_ACCESS_FAST: cv_AccessFlag = 67108864;
+#[doc = "! @addtogroup core_basic"]
+#[doc = "! @{"]
+pub type cv_AccessFlag = ::std::os::raw::c_uint;
+pub type cv_InputArray = [u64; 3usize];
+pub type cv_OutputArray = [u64; 3usize];
+pub const cv_UMatUsageFlags_USAGE_DEFAULT: cv_UMatUsageFlags = 0;
+pub const cv_UMatUsageFlags_USAGE_ALLOCATE_HOST_MEMORY: cv_UMatUsageFlags = 1;
+pub const cv_UMatUsageFlags_USAGE_ALLOCATE_DEVICE_MEMORY: cv_UMatUsageFlags = 2;
+pub const cv_UMatUsageFlags_USAGE_ALLOCATE_SHARED_MEMORY: cv_UMatUsageFlags = 4;
+pub const cv_UMatUsageFlags___UMAT_USAGE_FLAGS_32BIT: cv_UMatUsageFlags = 2147483647;
+#[doc = "! Usage flags for allocator"]
+pub type cv_UMatUsageFlags = ::std::os::raw::c_uint;
+#[doc = " @brief  Custom array allocator"]
+#[repr(C)]
+#[repr(align(8))]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_MatAllocator {
+    pub _bindgen_opaque_blob: u64,
+}
+#[test]
+fn bindgen_test_layout_cv_MatAllocator() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_MatAllocator>(),
+        8usize,
+        concat!("Size of: ", stringify!(cv_MatAllocator))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_MatAllocator>(),
+        8usize,
+        concat!("Alignment of ", stringify!(cv_MatAllocator))
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv12MatAllocator3mapEPNS_8UMatDataENS_10AccessFlagE"]
+    pub fn cv_MatAllocator_map(
+        this: *mut ::std::os::raw::c_void,
+        data: *mut cv_UMatData,
+        accessflags: cv_AccessFlag,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv12MatAllocator5unmapEPNS_8UMatDataE"]
+    pub fn cv_MatAllocator_unmap(this: *mut ::std::os::raw::c_void, data: *mut cv_UMatData);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv12MatAllocator8downloadEPNS_8UMatDataEPviPKmS5_S5_S5_"]
+    pub fn cv_MatAllocator_download(
+        this: *mut ::std::os::raw::c_void,
+        data: *mut cv_UMatData,
+        dst: *mut ::std::os::raw::c_void,
+        dims: ::std::os::raw::c_int,
+        sz: *const size_t,
+        srcofs: *const size_t,
+        srcstep: *const size_t,
+        dststep: *const size_t,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv12MatAllocator6uploadEPNS_8UMatDataEPKviPKmS6_S6_S6_"]
+    pub fn cv_MatAllocator_upload(
+        this: *mut ::std::os::raw::c_void,
+        data: *mut cv_UMatData,
+        src: *const ::std::os::raw::c_void,
+        dims: ::std::os::raw::c_int,
+        sz: *const size_t,
+        dstofs: *const size_t,
+        dststep: *const size_t,
+        srcstep: *const size_t,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv12MatAllocator4copyEPNS_8UMatDataES2_iPKmS4_S4_S4_S4_b"]
+    pub fn cv_MatAllocator_copy(
+        this: *mut ::std::os::raw::c_void,
+        srcdata: *mut cv_UMatData,
+        dstdata: *mut cv_UMatData,
+        dims: ::std::os::raw::c_int,
+        sz: *const size_t,
+        srcofs: *const size_t,
+        srcstep: *const size_t,
+        dstofs: *const size_t,
+        dststep: *const size_t,
+        sync: bool,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv12MatAllocator23getBufferPoolControllerEPKc"]
+    pub fn cv_MatAllocator_getBufferPoolController(
+        this: *mut ::std::os::raw::c_void,
+        id: *const ::std::os::raw::c_char,
+    ) -> *mut cv_BufferPoolController;
+}
+#[repr(C)]
+#[repr(align(8))]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_UMatData {
+    pub _bindgen_opaque_blob: [u64; 13usize],
+}
+pub const cv_UMatData_MemoryFlag_COPY_ON_MAP: cv_UMatData_MemoryFlag = 1;
+pub const cv_UMatData_MemoryFlag_HOST_COPY_OBSOLETE: cv_UMatData_MemoryFlag = 2;
+pub const cv_UMatData_MemoryFlag_DEVICE_COPY_OBSOLETE: cv_UMatData_MemoryFlag = 4;
+pub const cv_UMatData_MemoryFlag_TEMP_UMAT: cv_UMatData_MemoryFlag = 8;
+pub const cv_UMatData_MemoryFlag_TEMP_COPIED_UMAT: cv_UMatData_MemoryFlag = 24;
+pub const cv_UMatData_MemoryFlag_USER_ALLOCATED: cv_UMatData_MemoryFlag = 32;
+pub const cv_UMatData_MemoryFlag_DEVICE_MEM_MAPPED: cv_UMatData_MemoryFlag = 64;
+pub const cv_UMatData_MemoryFlag_ASYNC_CLEANUP: cv_UMatData_MemoryFlag = 128;
+pub type cv_UMatData_MemoryFlag = ::std::os::raw::c_uint;
+#[test]
+fn bindgen_test_layout_cv_UMatData() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_UMatData>(),
+        104usize,
+        concat!("Size of: ", stringify!(cv_UMatData))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_UMatData>(),
+        8usize,
+        concat!("Alignment of ", stringify!(cv_UMatData))
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv8UMatData4lockEv"]
+    pub fn cv_UMatData_lock(this: *mut cv_UMatData);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv8UMatData6unlockEv"]
+    pub fn cv_UMatData_unlock(this: *mut cv_UMatData);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv8UMatData16hostCopyObsoleteEv"]
+    pub fn cv_UMatData_hostCopyObsolete(this: *const cv_UMatData) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv8UMatData18deviceCopyObsoleteEv"]
+    pub fn cv_UMatData_deviceCopyObsolete(this: *const cv_UMatData) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv8UMatData15deviceMemMappedEv"]
+    pub fn cv_UMatData_deviceMemMapped(this: *const cv_UMatData) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv8UMatData9copyOnMapEv"]
+    pub fn cv_UMatData_copyOnMap(this: *const cv_UMatData) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv8UMatData8tempUMatEv"]
+    pub fn cv_UMatData_tempUMat(this: *const cv_UMatData) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv8UMatData14tempCopiedUMatEv"]
+    pub fn cv_UMatData_tempCopiedUMat(this: *const cv_UMatData) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv8UMatData20markHostCopyObsoleteEb"]
+    pub fn cv_UMatData_markHostCopyObsolete(this: *mut cv_UMatData, flag: bool);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv8UMatData22markDeviceCopyObsoleteEb"]
+    pub fn cv_UMatData_markDeviceCopyObsolete(this: *mut cv_UMatData, flag: bool);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv8UMatData19markDeviceMemMappedEb"]
+    pub fn cv_UMatData_markDeviceMemMapped(this: *mut cv_UMatData, flag: bool);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv8UMatDataC1EPKNS_12MatAllocatorE"]
+    pub fn cv_UMatData_UMatData(this: *mut cv_UMatData, allocator: *const cv_MatAllocator);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv8UMatDataD1Ev"]
+    pub fn cv_UMatData_UMatData_destructor(this: *mut cv_UMatData);
+}
+impl cv_UMatData {
+    #[inline]
+    pub unsafe fn lock(&mut self) {
+        cv_UMatData_lock(self)
+    }
+    #[inline]
+    pub unsafe fn unlock(&mut self) {
+        cv_UMatData_unlock(self)
+    }
+    #[inline]
+    pub unsafe fn hostCopyObsolete(&self) -> bool {
+        cv_UMatData_hostCopyObsolete(self)
+    }
+    #[inline]
+    pub unsafe fn deviceCopyObsolete(&self) -> bool {
+        cv_UMatData_deviceCopyObsolete(self)
+    }
+    #[inline]
+    pub unsafe fn deviceMemMapped(&self) -> bool {
+        cv_UMatData_deviceMemMapped(self)
+    }
+    #[inline]
+    pub unsafe fn copyOnMap(&self) -> bool {
+        cv_UMatData_copyOnMap(self)
+    }
+    #[inline]
+    pub unsafe fn tempUMat(&self) -> bool {
+        cv_UMatData_tempUMat(self)
+    }
+    #[inline]
+    pub unsafe fn tempCopiedUMat(&self) -> bool {
+        cv_UMatData_tempCopiedUMat(self)
+    }
+    #[inline]
+    pub unsafe fn markHostCopyObsolete(&mut self, flag: bool) {
+        cv_UMatData_markHostCopyObsolete(self, flag)
+    }
+    #[inline]
+    pub unsafe fn markDeviceCopyObsolete(&mut self, flag: bool) {
+        cv_UMatData_markDeviceCopyObsolete(self, flag)
+    }
+    #[inline]
+    pub unsafe fn markDeviceMemMapped(&mut self, flag: bool) {
+        cv_UMatData_markDeviceMemMapped(self, flag)
+    }
+    #[inline]
+    pub unsafe fn new(allocator: *const cv_MatAllocator) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMatData_UMatData(__bindgen_tmp.as_mut_ptr(), allocator);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn destruct(&mut self) {
+        cv_UMatData_UMatData_destructor(self)
+    }
+}
+#[doc = " @brief n-dimensional dense array class \\anchor CVMat_Details"]
+#[doc = ""]
+#[doc = "The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It"]
+#[doc = "can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel"]
+#[doc = "volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms"]
+#[doc = "may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array"]
+#[doc = "`M.step[]`, so that the address of element \\f$(i_0,...,i_{M.dims-1})\\f$, where \\f$0\\leq i_k<M.size[k]\\f$, is"]
+#[doc = "computed as:"]
+#[doc = "\\f[addr(M_{i_0,...,i_{M.dims-1}}) = M.data + M.step[0]*i_0 + M.step[1]*i_1 + ... + M.step[M.dims-1]*i_{M.dims-1}\\f]"]
+#[doc = "In case of a 2-dimensional array, the above formula is reduced to:"]
+#[doc = "\\f[addr(M_{i,j}) = M.data + M.step[0]*i + M.step[1]*j\\f]"]
+#[doc = "Note that `M.step[i] >= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means"]
+#[doc = "that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane,"]
+#[doc = "and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() ."]
+#[doc = ""]
+#[doc = "So, the data layout in Mat is compatible with the majority of dense array types from the standard"]
+#[doc = "toolkits and SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others,"]
+#[doc = "that is, with any array that uses *steps* (or *strides*) to compute the position of a pixel."]
+#[doc = "Due to this compatibility, it is possible to make a Mat header for user-allocated data and process"]
+#[doc = "it in-place using OpenCV functions."]
+#[doc = ""]
+#[doc = "There are many different ways to create a Mat object. The most popular options are listed below:"]
+#[doc = ""]
+#[doc = "- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue])"]
+#[doc = "constructor. A new array of the specified size and type is allocated. type has the same meaning as"]
+#[doc = "in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2"]
+#[doc = "means a 2-channel (complex) floating-point array, and so on."]
+#[doc = "@code"]
+#[doc = "Mat M(7,7,CV_32FC2,Scalar(1,3));"]
+#[doc = "M.create(100,60,CV_8UC(15));"]
+#[doc = "@endcode"]
+#[doc = "As noted in the introduction to this chapter, create() allocates only a new array when the shape"]
+#[doc = "or type of the current array are different from the specified ones."]
+#[doc = ""]
+#[doc = "- Create a multi-dimensional array:"]
+#[doc = "@code"]
+#[doc = "int sz[] = {100, 100, 100};"]
+#[doc = "Mat bigCube(3, sz, CV_8U, Scalar::all(0));"]
+#[doc = "@endcode"]
+#[doc = "It passes the number of dimensions =1 to the Mat constructor but the created array will be"]
+#[doc = "2-dimensional with the number of columns set to 1. So, Mat::dims is always \\>= 2 (can also be 0"]
+#[doc = "when the array is empty)."]
+#[doc = ""]
+#[doc = "- Use a copy constructor or assignment operator where there can be an array or expression on the"]
+#[doc = "right side (see below). As noted in the introduction, the array assignment is an O(1) operation"]
+#[doc = "because it only copies the header and increases the reference counter. The Mat::clone() method can"]
+#[doc = "be used to get a full (deep) copy of the array when you need it."]
+#[doc = ""]
+#[doc = "- Construct a header for a part of another array. It can be a single row, single column, several"]
+#[doc = "rows, several columns, rectangular region in the array (called a *minor* in algebra) or a"]
+#[doc = "diagonal. Such operations are also O(1) because the new header references the same data. You can"]
+#[doc = "actually modify a part of the array using this feature, for example:"]
+#[doc = "@code"]
+#[doc = "M.row(3) = M.row(3) + M.row(5)*3;"]
+#[doc = "Mat M1 = M.col(1);"]
+#[doc = "M.col(7).copyTo(M1);"]
+#[doc = "Mat img(Size(320,240),CV_8UC3);"]
+#[doc = "Mat roi(img, Rect(10,10,100,100));"]
+#[doc = "roi = Scalar(0,255,0);"]
+#[doc = "@endcode"]
+#[doc = "Due to the additional datastart and dataend members, it is possible to compute a relative"]
+#[doc = "sub-array position in the main *container* array using locateROI():"]
+#[doc = "@code"]
+#[doc = "Mat A = Mat::eye(10, 10, CV_32S);"]
+#[doc = "Mat B = A(Range::all(), Range(1, 3));"]
+#[doc = "Mat C = B(Range(5, 9), Range::all());"]
+#[doc = "Size size; Point ofs;"]
+#[doc = "C.locateROI(size, ofs);"]
+#[doc = "@endcode"]
+#[doc = "As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted"]
+#[doc = "sub-matrices."]
+#[doc = ""]
+#[doc = "- Make a header for user-allocated data. It can be useful to do the following:"]
+#[doc = "-# Process \"foreign\" data using OpenCV (for example, when you implement a DirectShow\\* filter or"]
+#[doc = "a processing module for gstreamer, and so on). For example:"]
+#[doc = "@code"]
+#[doc = "Mat process_video_frame(const unsigned char* pixels,"]
+#[doc = "int width, int height, int step)"]
+#[doc = "{"]
+#[doc = "Mat img(height, width, CV_8UC3, (unsigned char*)pixels, step);"]
+#[doc = ""]
+#[doc = "Mat result;"]
+#[doc = "GaussianBlur(img, result, Size(7, 7), 1.5, 1.5);"]
+#[doc = ""]
+#[doc = "return result;"]
+#[doc = "}"]
+#[doc = "@endcode"]
+#[doc = "-# Quickly initialize small matrices and/or get a super-fast element access."]
+#[doc = "@code"]
+#[doc = "double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};"]
+#[doc = "Mat M = Mat(3, 3, CV_64F, m).inv();"]
+#[doc = "@endcode"]
+#[doc = "."]
+#[doc = ""]
+#[doc = "- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example:"]
+#[doc = "@code"]
+#[doc = "M += Mat::eye(M.rows, M.cols, CV_64F);"]
+#[doc = "@endcode"]
+#[doc = ""]
+#[doc = "- Use a comma-separated initializer:"]
+#[doc = "@code"]
+#[doc = "Mat M = (Mat_<double>(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);"]
+#[doc = "@endcode"]
+#[doc = "With this approach, you first call a constructor of the Mat class with the proper parameters, and"]
+#[doc = "then you just put `<< operator` followed by comma-separated values that can be constants,"]
+#[doc = "variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation"]
+#[doc = "errors."]
+#[doc = ""]
+#[doc = "Once the array is created, it is automatically managed via a reference-counting mechanism. If the"]
+#[doc = "array header is built on top of user-allocated data, you should handle the data by yourself. The"]
+#[doc = "array data is deallocated when no one points to it. If you want to release the data pointed by a"]
+#[doc = "array header before the array destructor is called, use Mat::release()."]
+#[doc = ""]
+#[doc = "The next important thing to learn about the array class is element access. This manual already"]
+#[doc = "described how to compute an address of each array element. Normally, you are not required to use the"]
+#[doc = "formula directly in the code. If you know the array element type (which can be retrieved using the"]
+#[doc = "method Mat::type() ), you can access the element \\f$M_{ij}\\f$ of a 2-dimensional array as:"]
+#[doc = "@code"]
+#[doc = "M.at<double>(i,j) += 1.f;"]
+#[doc = "@endcode"]
+#[doc = "assuming that `M` is a double-precision floating-point array. There are several variants of the method"]
+#[doc = "at for a different number of dimensions."]
+#[doc = ""]
+#[doc = "If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to"]
+#[doc = "the row first, and then just use the plain C operator [] :"]
+#[doc = "@code"]
+#[doc = "double sum=0;"]
+#[doc = "for(int i = 0; i < M.rows; i++)"]
+#[doc = "{"]
+#[doc = "const double* Mi = M.ptr<double>(i);"]
+#[doc = "for(int j = 0; j < M.cols; j++)"]
+#[doc = "sum += std::max(Mi[j], 0.);"]
+#[doc = "}"]
+#[doc = "@endcode"]
+#[doc = "Some operations, like the one above, do not actually depend on the array shape. They just process"]
+#[doc = "elements of an array one by one (or elements from multiple arrays that have the same coordinates,"]
+#[doc = "for example, array addition). Such operations are called *element-wise*. It makes sense to check"]
+#[doc = "whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If"]
+#[doc = "yes, process them as a long single row:"]
+#[doc = "@code"]
+#[doc = "double sum=0;"]
+#[doc = "int cols = M.cols, rows = M.rows;"]
+#[doc = "if(M.isContinuous())"]
+#[doc = "{"]
+#[doc = "cols *= rows;"]
+#[doc = "rows = 1;"]
+#[doc = "}"]
+#[doc = "for(int i = 0; i < rows; i++)"]
+#[doc = "{"]
+#[doc = "const double* Mi = M.ptr<double>(i);"]
+#[doc = "for(int j = 0; j < cols; j++)"]
+#[doc = "sum += std::max(Mi[j], 0.);"]
+#[doc = "}"]
+#[doc = "@endcode"]
+#[doc = "In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is"]
+#[doc = "smaller, which is especially noticeable in case of small matrices."]
+#[doc = ""]
+#[doc = "Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows:"]
+#[doc = "@code"]
+#[doc = "double sum=0;"]
+#[doc = "MatConstIterator_<double> it = M.begin<double>(), it_end = M.end<double>();"]
+#[doc = "for(; it != it_end; ++it)"]
+#[doc = "sum += std::max(*it, 0.);"]
+#[doc = "@endcode"]
+#[doc = "The matrix iterators are random-access iterators, so they can be passed to any STL algorithm,"]
+#[doc = "including std::sort()."]
+#[doc = ""]
+#[doc = "@note Matrix Expressions and arithmetic see MatExpr"]
+#[repr(C)]
+#[repr(align(8))]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_Mat {
+    pub _bindgen_opaque_blob: [u64; 12usize],
+}
+pub const cv_Mat_MAGIC_VAL: ::std::os::raw::c_uint = 1124007936;
+pub const cv_Mat_AUTO_STEP: ::std::os::raw::c_uint = 0;
+pub const cv_Mat_CONTINUOUS_FLAG: ::std::os::raw::c_uint = 16384;
+pub const cv_Mat_SUBMATRIX_FLAG: ::std::os::raw::c_uint = 32768;
+pub type cv_Mat__bindgen_ty_1 = ::std::os::raw::c_uint;
+pub const cv_Mat_MAGIC_MASK: ::std::os::raw::c_uint = 4294901760;
+pub const cv_Mat_TYPE_MASK: ::std::os::raw::c_uint = 4095;
+pub const cv_Mat_DEPTH_MASK: ::std::os::raw::c_uint = 7;
+pub type cv_Mat__bindgen_ty_2 = ::std::os::raw::c_uint;
+#[test]
+fn bindgen_test_layout_cv_Mat() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_Mat>(),
+        96usize,
+        concat!("Size of: ", stringify!(cv_Mat))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_Mat>(),
+        8usize,
+        concat!("Alignment of ", stringify!(cv_Mat))
+    );
+}
+extern "C" {
+    #[doc = "! retrieve UMat from Mat"]
+    #[link_name = "\u{1}_ZNK2cv3Mat7getUMatENS_10AccessFlagENS_14UMatUsageFlagsE"]
+    pub fn cv_Mat_getUMat(
+        this: *const cv_Mat,
+        accessFlags: cv_AccessFlag,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[doc = " @brief Creates a matrix header for the specified matrix row."]
+    #[doc = ""]
+    #[doc = "The method makes a new header for the specified matrix row and returns it. This is an O(1)"]
+    #[doc = "operation, regardless of the matrix size. The underlying data of the new matrix is shared with the"]
+    #[doc = "original matrix. Here is the example of one of the classical basic matrix processing operations,"]
+    #[doc = "axpy, used by LU and many other algorithms:"]
+    #[doc = "@code"]
+    #[doc = "inline void matrix_axpy(Mat& A, int i, int j, double alpha)"]
+    #[doc = "{"]
+    #[doc = "A.row(i) += A.row(j)*alpha;"]
+    #[doc = "}"]
+    #[doc = "@endcode"]
+    #[doc = "@note In the current implementation, the following code does not work as expected:"]
+    #[doc = "@code"]
+    #[doc = "Mat A;"]
+    #[doc = "..."]
+    #[doc = "A.row(i) = A.row(j); // will not work"]
+    #[doc = "@endcode"]
+    #[doc = "This happens because A.row(i) forms a temporary header that is further assigned to another header."]
+    #[doc = "Remember that each of these operations is O(1), that is, no data is copied. Thus, the above"]
+    #[doc = "assignment is not true if you may have expected the j-th row to be copied to the i-th row. To"]
+    #[doc = "achieve that, you should either turn this simple assignment into an expression or use the"]
+    #[doc = "Mat::copyTo method:"]
+    #[doc = "@code"]
+    #[doc = "Mat A;"]
+    #[doc = "..."]
+    #[doc = "A.row(i) = A.row(j) + 0;"]
+    #[doc = "A.row(j).copyTo(A.row(i));"]
+    #[doc = "@endcode"]
+    #[doc = "@param y A 0-based row index."]
+    #[link_name = "\u{1}_ZNK2cv3Mat3rowEi"]
+    pub fn cv_Mat_row(this: *const cv_Mat, y: ::std::os::raw::c_int) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Creates a matrix header for the specified matrix column."]
+    #[doc = ""]
+    #[doc = "The method makes a new header for the specified matrix column and returns it. This is an O(1)"]
+    #[doc = "operation, regardless of the matrix size. The underlying data of the new matrix is shared with the"]
+    #[doc = "original matrix. See also the Mat::row description."]
+    #[doc = "@param x A 0-based column index."]
+    #[link_name = "\u{1}_ZNK2cv3Mat3colEi"]
+    pub fn cv_Mat_col(this: *const cv_Mat, x: ::std::os::raw::c_int) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Creates a matrix header for the specified row span."]
+    #[doc = ""]
+    #[doc = "The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and"]
+    #[doc = "Mat::col , this is an O(1) operation."]
+    #[doc = "@param startrow An inclusive 0-based start index of the row span."]
+    #[doc = "@param endrow An exclusive 0-based ending index of the row span."]
+    #[link_name = "\u{1}_ZNK2cv3Mat8rowRangeEii"]
+    pub fn cv_Mat_rowRange(
+        this: *const cv_Mat,
+        startrow: ::std::os::raw::c_int,
+        endrow: ::std::os::raw::c_int,
+    ) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param r Range structure containing both the start and the end indices."]
+    #[link_name = "\u{1}_ZNK2cv3Mat8rowRangeERKNS_5RangeE"]
+    pub fn cv_Mat_rowRange1(this: *const cv_Mat, r: *const cv_Range) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Creates a matrix header for the specified column span."]
+    #[doc = ""]
+    #[doc = "The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and"]
+    #[doc = "Mat::col , this is an O(1) operation."]
+    #[doc = "@param startcol An inclusive 0-based start index of the column span."]
+    #[doc = "@param endcol An exclusive 0-based ending index of the column span."]
+    #[link_name = "\u{1}_ZNK2cv3Mat8colRangeEii"]
+    pub fn cv_Mat_colRange(
+        this: *const cv_Mat,
+        startcol: ::std::os::raw::c_int,
+        endcol: ::std::os::raw::c_int,
+    ) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param r Range structure containing both the start and the end indices."]
+    #[link_name = "\u{1}_ZNK2cv3Mat8colRangeERKNS_5RangeE"]
+    pub fn cv_Mat_colRange1(this: *const cv_Mat, r: *const cv_Range) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Extracts a diagonal from a matrix"]
+    #[doc = ""]
+    #[doc = "The method makes a new header for the specified matrix diagonal. The new matrix is represented as a"]
+    #[doc = "single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation."]
+    #[doc = "@param d index of the diagonal, with the following values:"]
+    #[doc = "- `d=0` is the main diagonal."]
+    #[doc = "- `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set"]
+    #[doc = "immediately below the main one."]
+    #[doc = "- `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set"]
+    #[doc = "immediately above the main one."]
+    #[doc = "For example:"]
+    #[doc = "@code"]
+    #[doc = "Mat m = (Mat_<int>(3,3) <<"]
+    #[doc = "1,2,3,"]
+    #[doc = "4,5,6,"]
+    #[doc = "7,8,9);"]
+    #[doc = "Mat d0 = m.diag(0);"]
+    #[doc = "Mat d1 = m.diag(1);"]
+    #[doc = "Mat d_1 = m.diag(-1);"]
+    #[doc = "@endcode"]
+    #[doc = "The resulting matrices are"]
+    #[doc = "@code"]
+    #[doc = "d0 ="]
+    #[doc = "[1;"]
+    #[doc = "5;"]
+    #[doc = "9]"]
+    #[doc = "d1 ="]
+    #[doc = "[2;"]
+    #[doc = "6]"]
+    #[doc = "d_1 ="]
+    #[doc = "[4;"]
+    #[doc = "8]"]
+    #[doc = "@endcode"]
+    #[link_name = "\u{1}_ZNK2cv3Mat4diagEi"]
+    pub fn cv_Mat_diag(this: *const cv_Mat, d: ::std::os::raw::c_int) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief creates a diagonal matrix"]
+    #[doc = ""]
+    #[doc = "The method creates a square diagonal matrix from specified main diagonal."]
+    #[doc = "@param d One-dimensional matrix that represents the main diagonal."]
+    #[link_name = "\u{1}_ZN2cv3Mat4diagERKS0_"]
+    pub fn cv_Mat_diag1(d: *const cv_Mat) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Creates a full copy of the array and the underlying data."]
+    #[doc = ""]
+    #[doc = "The method creates a full copy of the array. The original step[] is not taken into account. So, the"]
+    #[doc = "array copy is a continuous array occupying total()*elemSize() bytes."]
+    #[link_name = "\u{1}_ZNK2cv3Mat5cloneEv"]
+    pub fn cv_Mat_clone(this: *const cv_Mat) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Copies the matrix to another one."]
+    #[doc = ""]
+    #[doc = "The method copies the matrix data to another matrix. Before copying the data, the method invokes :"]
+    #[doc = "@code"]
+    #[doc = "m.create(this->size(), this->type());"]
+    #[doc = "@endcode"]
+    #[doc = "so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the"]
+    #[doc = "function does not handle the case of a partial overlap between the source and the destination"]
+    #[doc = "matrices."]
+    #[doc = ""]
+    #[doc = "When the operation mask is specified, if the Mat::create call shown above reallocates the matrix,"]
+    #[doc = "the newly allocated matrix is initialized with all zeros before copying the data."]
+    #[doc = "@param m Destination matrix. If it does not have a proper size or type before the operation, it is"]
+    #[doc = "reallocated."]
+    #[link_name = "\u{1}_ZNK2cv3Mat6copyToERKNS_12_OutputArrayE"]
+    pub fn cv_Mat_copyTo(this: *const cv_Mat, m: cv_OutputArray);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param m Destination matrix. If it does not have a proper size or type before the operation, it is"]
+    #[doc = "reallocated."]
+    #[doc = "@param mask Operation mask of the same size as \\*this. Its non-zero elements indicate which matrix"]
+    #[doc = "elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels."]
+    #[link_name = "\u{1}_ZNK2cv3Mat6copyToERKNS_12_OutputArrayERKNS_11_InputArrayE"]
+    pub fn cv_Mat_copyTo1(this: *const cv_Mat, m: cv_OutputArray, mask: cv_InputArray);
+}
+extern "C" {
+    #[doc = " @brief Converts an array to another data type with optional scaling."]
+    #[doc = ""]
+    #[doc = "The method converts source pixel values to the target data type. saturate_cast\\<\\> is applied at"]
+    #[doc = "the end to avoid possible overflows:"]
+    #[doc = ""]
+    #[doc = "\\f[m(x,y) = saturate \\_ cast<rType>( \\alpha (*this)(x,y) +  \\beta )\\f]"]
+    #[doc = "@param m output matrix; if it does not have a proper size or type before the operation, it is"]
+    #[doc = "reallocated."]
+    #[doc = "@param rtype desired output matrix type or, rather, the depth since the number of channels are the"]
+    #[doc = "same as the input has; if rtype is negative, the output matrix will have the same type as the input."]
+    #[doc = "@param alpha optional scale factor."]
+    #[doc = "@param beta optional delta added to the scaled values."]
+    #[link_name = "\u{1}_ZNK2cv3Mat9convertToERKNS_12_OutputArrayEidd"]
+    pub fn cv_Mat_convertTo(
+        this: *const cv_Mat,
+        m: cv_OutputArray,
+        rtype: ::std::os::raw::c_int,
+        alpha: f64,
+        beta: f64,
+    );
+}
+extern "C" {
+    #[doc = " @brief Provides a functional form of convertTo."]
+    #[doc = ""]
+    #[doc = "This is an internally used method called by the @ref MatrixExpressions engine."]
+    #[doc = "@param m Destination array."]
+    #[doc = "@param type Desired destination array depth (or -1 if it should be the same as the source type)."]
+    #[link_name = "\u{1}_ZNK2cv3Mat8assignToERS0_i"]
+    pub fn cv_Mat_assignTo(this: *const cv_Mat, m: *mut cv_Mat, type_: ::std::os::raw::c_int);
+}
+extern "C" {
+    #[doc = " @brief Sets all or some of the array elements to the specified value."]
+    #[doc = ""]
+    #[doc = "This is an advanced variant of the Mat::operator=(const Scalar& s) operator."]
+    #[doc = "@param value Assigned scalar converted to the actual array type."]
+    #[doc = "@param mask Operation mask of the same size as \\*this. Its non-zero elements indicate which matrix"]
+    #[doc = "elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels"]
+    #[link_name = "\u{1}_ZN2cv3Mat5setToERKNS_11_InputArrayES3_"]
+    pub fn cv_Mat_setTo(
+        this: *mut cv_Mat,
+        value: cv_InputArray,
+        mask: cv_InputArray,
+    ) -> *mut cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data."]
+    #[doc = ""]
+    #[doc = "The method makes a new matrix header for \\*this elements. The new matrix may have a different size"]
+    #[doc = "and/or different number of channels. Any combination is possible if:"]
+    #[doc = "-   No extra elements are included into the new matrix and no elements are excluded. Consequently,"]
+    #[doc = "the product rows\\*cols\\*channels() must stay the same after the transformation."]
+    #[doc = "-   No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of"]
+    #[doc = "rows, or the operation changes the indices of elements row in some other way, the matrix must be"]
+    #[doc = "continuous. See Mat::isContinuous ."]
+    #[doc = ""]
+    #[doc = "For example, if there is a set of 3D points stored as an STL vector, and you want to represent the"]
+    #[doc = "points as a 3xN matrix, do the following:"]
+    #[doc = "@code"]
+    #[doc = "std::vector<Point3f> vec;"]
+    #[doc = "..."]
+    #[doc = "Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation"]
+    #[doc = "reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel."]
+    #[doc = "t(); // finally, transpose the Nx3 matrix."]
+    #[doc = "@endcode"]
+    #[doc = "@param cn New number of channels. If the parameter is 0, the number of channels remains the same."]
+    #[doc = "@param rows New number of rows. If the parameter is 0, the number of rows remains the same."]
+    #[link_name = "\u{1}_ZNK2cv3Mat7reshapeEii"]
+    pub fn cv_Mat_reshape(
+        this: *const cv_Mat,
+        cn: ::std::os::raw::c_int,
+        rows: ::std::os::raw::c_int,
+    ) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[link_name = "\u{1}_ZNK2cv3Mat7reshapeEiiPKi"]
+    pub fn cv_Mat_reshape1(
+        this: *const cv_Mat,
+        cn: ::std::os::raw::c_int,
+        newndims: ::std::os::raw::c_int,
+        newsz: *const ::std::os::raw::c_int,
+    ) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[link_name = "\u{1}_ZNK2cv3Mat7reshapeEiRKSt6vectorIiSaIiEE"]
+    pub fn cv_Mat_reshape2(
+        this: *const cv_Mat,
+        cn: ::std::os::raw::c_int,
+        newshape: *const [u64; 3usize],
+    ) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Transposes a matrix."]
+    #[doc = ""]
+    #[doc = "The method performs matrix transposition by means of matrix expressions. It does not perform the"]
+    #[doc = "actual transposition but returns a temporary matrix transposition object that can be further used as"]
+    #[doc = "a part of more complex matrix expressions or can be assigned to a matrix:"]
+    #[doc = "@code"]
+    #[doc = "Mat A1 = A + Mat::eye(A.size(), A.type())*lambda;"]
+    #[doc = "Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I)"]
+    #[doc = "@endcode"]
+    #[link_name = "\u{1}_ZNK2cv3Mat1tEv"]
+    pub fn cv_Mat_t(this: *const cv_Mat) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @brief Inverses a matrix."]
+    #[doc = ""]
+    #[doc = "The method performs a matrix inversion by means of matrix expressions. This means that a temporary"]
+    #[doc = "matrix inversion object is returned by the method and can be used further as a part of more complex"]
+    #[doc = "matrix expressions or can be assigned to a matrix."]
+    #[doc = "@param method Matrix inversion method. One of cv::DecompTypes"]
+    #[link_name = "\u{1}_ZNK2cv3Mat3invEi"]
+    pub fn cv_Mat_inv(this: *const cv_Mat, method: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @brief Performs an element-wise multiplication or division of the two matrices."]
+    #[doc = ""]
+    #[doc = "The method returns a temporary object encoding per-element array multiplication, with optional"]
+    #[doc = "scale. Note that this is not a matrix multiplication that corresponds to a simpler \"\\*\" operator."]
+    #[doc = ""]
+    #[doc = "Example:"]
+    #[doc = "@code"]
+    #[doc = "Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5)"]
+    #[doc = "@endcode"]
+    #[doc = "@param m Another array of the same type and the same size as \\*this, or a matrix expression."]
+    #[doc = "@param scale Optional scale factor."]
+    #[link_name = "\u{1}_ZNK2cv3Mat3mulERKNS_11_InputArrayEd"]
+    pub fn cv_Mat_mul(this: *const cv_Mat, m: cv_InputArray, scale: f64) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @brief Computes a cross-product of two 3-element vectors."]
+    #[doc = ""]
+    #[doc = "The method computes a cross-product of two 3-element vectors. The vectors must be 3-element"]
+    #[doc = "floating-point vectors of the same shape and size. The result is another 3-element vector of the"]
+    #[doc = "same shape and type as operands."]
+    #[doc = "@param m Another cross-product operand."]
+    #[link_name = "\u{1}_ZNK2cv3Mat5crossERKNS_11_InputArrayE"]
+    pub fn cv_Mat_cross(this: *const cv_Mat, m: cv_InputArray) -> cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Computes a dot-product of two vectors."]
+    #[doc = ""]
+    #[doc = "The method computes a dot-product of two matrices. If the matrices are not single-column or"]
+    #[doc = "single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D"]
+    #[doc = "vectors. The vectors must have the same size and type. If the matrices have more than one channel,"]
+    #[doc = "the dot products from all the channels are summed together."]
+    #[doc = "@param m another dot-product operand."]
+    #[link_name = "\u{1}_ZNK2cv3Mat3dotERKNS_11_InputArrayE"]
+    pub fn cv_Mat_dot(this: *const cv_Mat, m: cv_InputArray) -> f64;
+}
+extern "C" {
+    #[doc = " @brief Returns a zero array of the specified size and type."]
+    #[doc = ""]
+    #[doc = "The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant"]
+    #[doc = "array as a function parameter, part of a matrix expression, or as a matrix initializer:"]
+    #[doc = "@code"]
+    #[doc = "Mat A;"]
+    #[doc = "A = Mat::zeros(3, 3, CV_32F);"]
+    #[doc = "@endcode"]
+    #[doc = "In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix."]
+    #[doc = "Otherwise, the existing matrix A is filled with zeros."]
+    #[doc = "@param rows Number of rows."]
+    #[doc = "@param cols Number of columns."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat5zerosEiii"]
+    pub fn cv_Mat_zeros(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param size Alternative to the matrix size specification Size(cols, rows) ."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat5zerosENS_5Size_IiEEi"]
+    pub fn cv_Mat_zeros1(size: cv_Size, type_: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param ndims Array dimensionality."]
+    #[doc = "@param sz Array of integers specifying the array shape."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat5zerosEiPKii"]
+    pub fn cv_Mat_zeros2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @brief Returns an array of all 1's of the specified size and type."]
+    #[doc = ""]
+    #[doc = "The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using"]
+    #[doc = "this method you can initialize an array with an arbitrary value, using the following Matlab idiom:"]
+    #[doc = "@code"]
+    #[doc = "Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3."]
+    #[doc = "@endcode"]
+    #[doc = "The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it"]
+    #[doc = "just remembers the scale factor (3 in this case) and use it when actually invoking the matrix"]
+    #[doc = "initializer."]
+    #[doc = "@note In case of multi-channels type, only the first channel will be initialized with 1's, the"]
+    #[doc = "others will be set to 0's."]
+    #[doc = "@param rows Number of rows."]
+    #[doc = "@param cols Number of columns."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat4onesEiii"]
+    pub fn cv_Mat_ones(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param size Alternative to the matrix size specification Size(cols, rows) ."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat4onesENS_5Size_IiEEi"]
+    pub fn cv_Mat_ones1(size: cv_Size, type_: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param ndims Array dimensionality."]
+    #[doc = "@param sz Array of integers specifying the array shape."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat4onesEiPKii"]
+    pub fn cv_Mat_ones2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @brief Returns an identity matrix of the specified size and type."]
+    #[doc = ""]
+    #[doc = "The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to"]
+    #[doc = "Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently:"]
+    #[doc = "@code"]
+    #[doc = "Mat A = Mat::eye(4, 4, CV_32F)*0.1;"]
+    #[doc = "@endcode"]
+    #[doc = "@note In case of multi-channels type, identity matrix will be initialized only for the first channel,"]
+    #[doc = "the others will be set to 0's"]
+    #[doc = "@param rows Number of rows."]
+    #[doc = "@param cols Number of columns."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat3eyeEiii"]
+    pub fn cv_Mat_eye(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param size Alternative matrix size specification as Size(cols, rows) ."]
+    #[doc = "@param type Created matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat3eyeENS_5Size_IiEEi"]
+    pub fn cv_Mat_eye1(size: cv_Size, type_: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[doc = " @brief Allocates new array data if needed."]
+    #[doc = ""]
+    #[doc = "This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays"]
+    #[doc = "call this method for each output array. The method uses the following algorithm:"]
+    #[doc = ""]
+    #[doc = "-# If the current array shape and the type match the new ones, return immediately. Otherwise,"]
+    #[doc = "de-reference the previous data by calling Mat::release."]
+    #[doc = "-# Initialize the new header."]
+    #[doc = "-# Allocate the new data of total()\\*elemSize() bytes."]
+    #[doc = "-# Allocate the new, associated with the data, reference counter and set it to 1."]
+    #[doc = ""]
+    #[doc = "Such a scheme makes the memory management robust and efficient at the same time and helps avoid"]
+    #[doc = "extra typing for you. This means that usually there is no need to explicitly allocate output arrays."]
+    #[doc = "That is, instead of writing:"]
+    #[doc = "@code"]
+    #[doc = "Mat color;"]
+    #[doc = "..."]
+    #[doc = "Mat gray(color.rows, color.cols, color.depth());"]
+    #[doc = "cvtColor(color, gray, COLOR_BGR2GRAY);"]
+    #[doc = "@endcode"]
+    #[doc = "you can simply write:"]
+    #[doc = "@code"]
+    #[doc = "Mat color;"]
+    #[doc = "..."]
+    #[doc = "Mat gray;"]
+    #[doc = "cvtColor(color, gray, COLOR_BGR2GRAY);"]
+    #[doc = "@endcode"]
+    #[doc = "because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array"]
+    #[doc = "internally."]
+    #[doc = "@param rows New number of rows."]
+    #[doc = "@param cols New number of columns."]
+    #[doc = "@param type New matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat6createEiii"]
+    pub fn cv_Mat_create(
+        this: *mut cv_Mat,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param size Alternative new matrix size specification: Size(cols, rows)"]
+    #[doc = "@param type New matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat6createENS_5Size_IiEEi"]
+    pub fn cv_Mat_create1(this: *mut cv_Mat, size: cv_Size, type_: ::std::os::raw::c_int);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param ndims New array dimensionality."]
+    #[doc = "@param sizes Array of integers specifying a new array shape."]
+    #[doc = "@param type New matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat6createEiPKii"]
+    pub fn cv_Mat_create2(
+        this: *mut cv_Mat,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param sizes Array of integers specifying a new array shape."]
+    #[doc = "@param type New matrix type."]
+    #[link_name = "\u{1}_ZN2cv3Mat6createERKSt6vectorIiSaIiEEi"]
+    pub fn cv_Mat_create3(
+        this: *mut cv_Mat,
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+    );
+}
+extern "C" {
+    #[doc = " @brief Increments the reference counter."]
+    #[doc = ""]
+    #[doc = "The method increments the reference counter associated with the matrix data. If the matrix header"]
+    #[doc = "points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no"]
+    #[doc = "effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It"]
+    #[doc = "is called implicitly by the matrix assignment operator. The reference counter increment is an atomic"]
+    #[doc = "operation on the platforms that support it. Thus, it is safe to operate on the same matrices"]
+    #[doc = "asynchronously in different threads."]
+    #[link_name = "\u{1}_ZN2cv3Mat6addrefEv"]
+    pub fn cv_Mat_addref(this: *mut cv_Mat);
+}
+extern "C" {
+    #[doc = " @brief Decrements the reference counter and deallocates the matrix if needed."]
+    #[doc = ""]
+    #[doc = "The method decrements the reference counter associated with the matrix data. When the reference"]
+    #[doc = "counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers"]
+    #[doc = "are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the"]
+    #[doc = "reference counter is NULL, and the method has no effect in this case."]
+    #[doc = ""]
+    #[doc = "This method can be called manually to force the matrix data deallocation. But since this method is"]
+    #[doc = "automatically called in the destructor, or by any other method that changes the data pointer, it is"]
+    #[doc = "usually not needed. The reference counter decrement and check for 0 is an atomic operation on the"]
+    #[doc = "platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in"]
+    #[doc = "different threads."]
+    #[link_name = "\u{1}_ZN2cv3Mat7releaseEv"]
+    pub fn cv_Mat_release(this: *mut cv_Mat);
+}
+extern "C" {
+    #[doc = "! internal use function, consider to use 'release' method instead; deallocates the matrix data"]
+    #[link_name = "\u{1}_ZN2cv3Mat10deallocateEv"]
+    pub fn cv_Mat_deallocate(this: *mut cv_Mat);
+}
+extern "C" {
+    #[doc = "! internal use function; properly re-allocates _size, _step arrays"]
+    #[link_name = "\u{1}_ZN2cv3Mat8copySizeERKS0_"]
+    pub fn cv_Mat_copySize(this: *mut cv_Mat, m: *const cv_Mat);
+}
+extern "C" {
+    #[doc = " @brief Reserves space for the certain number of rows."]
+    #[doc = ""]
+    #[doc = "The method reserves space for sz rows. If the matrix already has enough space to store sz rows,"]
+    #[doc = "nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method"]
+    #[doc = "emulates the corresponding method of the STL vector class."]
+    #[doc = "@param sz Number of rows."]
+    #[link_name = "\u{1}_ZN2cv3Mat7reserveEm"]
+    pub fn cv_Mat_reserve(this: *mut cv_Mat, sz: size_t);
+}
+extern "C" {
+    #[doc = " @brief Reserves space for the certain number of bytes."]
+    #[doc = ""]
+    #[doc = "The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes,"]
+    #[doc = "nothing happens. If matrix has to be reallocated its previous content could be lost."]
+    #[doc = "@param sz Number of bytes."]
+    #[link_name = "\u{1}_ZN2cv3Mat13reserveBufferEm"]
+    pub fn cv_Mat_reserveBuffer(this: *mut cv_Mat, sz: size_t);
+}
+extern "C" {
+    #[doc = " @brief Changes the number of matrix rows."]
+    #[doc = ""]
+    #[doc = "The methods change the number of matrix rows. If the matrix is reallocated, the first"]
+    #[doc = "min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL"]
+    #[doc = "vector class."]
+    #[doc = "@param sz New number of rows."]
+    #[link_name = "\u{1}_ZN2cv3Mat6resizeEm"]
+    pub fn cv_Mat_resize(this: *mut cv_Mat, sz: size_t);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param sz New number of rows."]
+    #[doc = "@param s Value assigned to the newly added elements."]
+    #[link_name = "\u{1}_ZN2cv3Mat6resizeEmRKNS_7Scalar_IdEE"]
+    pub fn cv_Mat_resize1(this: *mut cv_Mat, sz: size_t, s: *const cv_Scalar);
+}
+extern "C" {
+    #[doc = "! internal function"]
+    #[link_name = "\u{1}_ZN2cv3Mat10push_back_EPKv"]
+    pub fn cv_Mat_push_back_(this: *mut cv_Mat, elem: *const ::std::os::raw::c_void);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param m Added line(s)."]
+    #[link_name = "\u{1}_ZN2cv3Mat9push_backERKS0_"]
+    pub fn cv_Mat_push_back(this: *mut cv_Mat, m: *const cv_Mat);
+}
+extern "C" {
+    #[doc = " @brief Removes elements from the bottom of the matrix."]
+    #[doc = ""]
+    #[doc = "The method removes one or more rows from the bottom of the matrix."]
+    #[doc = "@param nelems Number of removed rows. If it is greater than the total number of rows, an exception"]
+    #[doc = "is thrown."]
+    #[link_name = "\u{1}_ZN2cv3Mat8pop_backEm"]
+    pub fn cv_Mat_pop_back(this: *mut cv_Mat, nelems: size_t);
+}
+extern "C" {
+    #[doc = " @brief Locates the matrix header within a parent matrix."]
+    #[doc = ""]
+    #[doc = "After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange,"]
+    #[doc = "Mat::colRange, and others, the resultant submatrix points just to the part of the original big"]
+    #[doc = "matrix. However, each submatrix contains information (represented by datastart and dataend"]
+    #[doc = "fields) that helps reconstruct the original matrix size and the position of the extracted"]
+    #[doc = "submatrix within the original matrix. The method locateROI does exactly that."]
+    #[doc = "@param wholeSize Output parameter that contains the size of the whole matrix containing *this*"]
+    #[doc = "as a part."]
+    #[doc = "@param ofs Output parameter that contains an offset of *this* inside the whole matrix."]
+    #[link_name = "\u{1}_ZNK2cv3Mat9locateROIERNS_5Size_IiEERNS_6Point_IiEE"]
+    pub fn cv_Mat_locateROI(this: *const cv_Mat, wholeSize: *mut cv_Size, ofs: *mut cv_Point);
+}
+extern "C" {
+    #[doc = " @brief Adjusts a submatrix size and position within the parent matrix."]
+    #[doc = ""]
+    #[doc = "The method is complimentary to Mat::locateROI . The typical use of these functions is to determine"]
+    #[doc = "the submatrix position within the parent matrix and then shift the position somehow. Typically, it"]
+    #[doc = "can be required for filtering operations when pixels outside of the ROI should be taken into"]
+    #[doc = "account. When all the method parameters are positive, the ROI needs to grow in all directions by the"]
+    #[doc = "specified amount, for example:"]
+    #[doc = "@code"]
+    #[doc = "A.adjustROI(2, 2, 2, 2);"]
+    #[doc = "@endcode"]
+    #[doc = "In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted"]
+    #[doc = "by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the"]
+    #[doc = "filtering with the 5x5 kernel."]
+    #[doc = ""]
+    #[doc = "adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the"]
+    #[doc = "adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is"]
+    #[doc = "located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not"]
+    #[doc = "be increased in the upward direction."]
+    #[doc = ""]
+    #[doc = "The function is used internally by the OpenCV filtering functions, like filter2D , morphological"]
+    #[doc = "operations, and so on."]
+    #[doc = "@param dtop Shift of the top submatrix boundary upwards."]
+    #[doc = "@param dbottom Shift of the bottom submatrix boundary downwards."]
+    #[doc = "@param dleft Shift of the left submatrix boundary to the left."]
+    #[doc = "@param dright Shift of the right submatrix boundary to the right."]
+    #[doc = "@sa copyMakeBorder"]
+    #[link_name = "\u{1}_ZN2cv3Mat9adjustROIEiiii"]
+    pub fn cv_Mat_adjustROI(
+        this: *mut cv_Mat,
+        dtop: ::std::os::raw::c_int,
+        dbottom: ::std::os::raw::c_int,
+        dleft: ::std::os::raw::c_int,
+        dright: ::std::os::raw::c_int,
+    ) -> *mut cv_Mat;
+}
+extern "C" {
+    #[doc = " @brief Reports whether the matrix is continuous or not."]
+    #[doc = ""]
+    #[doc = "The method returns true if the matrix elements are stored continuously without gaps at the end of"]
+    #[doc = "each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous."]
+    #[doc = "Matrices created with Mat::create are always continuous. But if you extract a part of the matrix"]
+    #[doc = "using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data,"]
+    #[doc = "such matrices may no longer have this property."]
+    #[doc = ""]
+    #[doc = "The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when"]
+    #[doc = "you construct a matrix header. Thus, the continuity check is a very fast operation, though"]
+    #[doc = "theoretically it could be done as follows:"]
+    #[doc = "@code"]
+    #[doc = "bool myCheckMatContinuity(const Mat& m)"]
+    #[doc = "{"]
+    #[doc = "return m.rows == 1 || m.step == m.cols*m.elemSize();"]
+    #[doc = "}"]
+    #[doc = "@endcode"]
+    #[doc = "The method is used in quite a few of OpenCV functions. The point is that element-wise operations"]
+    #[doc = "(such as arithmetic and logical operations, math functions, alpha blending, color space"]
+    #[doc = "transformations, and others) do not depend on the image geometry. Thus, if all the input and output"]
+    #[doc = "arrays are continuous, the functions can process them as very long single-row vectors. The example"]
+    #[doc = "below illustrates how an alpha-blending function can be implemented:"]
+    #[doc = "@code"]
+    #[doc = "template<typename T>"]
+    #[doc = "void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst)"]
+    #[doc = "{"]
+    #[doc = "const float alpha_scale = (float)std::numeric_limits<T>::max(),"]
+    #[doc = "inv_scale = 1.f/alpha_scale;"]
+    #[doc = ""]
+    #[doc = "CV_Assert( src1.type() == src2.type() &&"]
+    #[doc = "src1.type() == CV_MAKETYPE(traits::Depth<T>::value, 4) &&"]
+    #[doc = "src1.size() == src2.size());"]
+    #[doc = "Size size = src1.size();"]
+    #[doc = "dst.create(size, src1.type());"]
+    #[doc = ""]
+    #[doc = "if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() )"]
+    #[doc = "{"]
+    #[doc = "size.width *= size.height;"]
+    #[doc = "size.height = 1;"]
+    #[doc = "}"]
+    #[doc = "size.width *= 4;"]
+    #[doc = ""]
+    #[doc = "for( int i = 0; i < size.height; i++ )"]
+    #[doc = "{"]
+    #[doc = "const T* ptr1 = src1.ptr<T>(i);"]
+    #[doc = "const T* ptr2 = src2.ptr<T>(i);"]
+    #[doc = "T* dptr = dst.ptr<T>(i);"]
+    #[doc = ""]
+    #[doc = "for( int j = 0; j < size.width; j += 4 )"]
+    #[doc = "{"]
+    #[doc = "float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale;"]
+    #[doc = "dptr[j] = saturate_cast<T>(ptr1[j]*alpha + ptr2[j]*beta);"]
+    #[doc = "dptr[j+1] = saturate_cast<T>(ptr1[j+1]*alpha + ptr2[j+1]*beta);"]
+    #[doc = "dptr[j+2] = saturate_cast<T>(ptr1[j+2]*alpha + ptr2[j+2]*beta);"]
+    #[doc = "dptr[j+3] = saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale);"]
+    #[doc = "}"]
+    #[doc = "}"]
+    #[doc = "}"]
+    #[doc = "@endcode"]
+    #[doc = "This approach, while being very simple, can boost the performance of a simple element-operation by"]
+    #[doc = "10-20 percents, especially if the image is rather small and the operation is quite simple."]
+    #[doc = ""]
+    #[doc = "Another OpenCV idiom in this function, a call of Mat::create for the destination array, that"]
+    #[doc = "allocates the destination array unless it already has the proper size and type. And while the newly"]
+    #[doc = "allocated arrays are always continuous, you still need to check the destination array because"]
+    #[doc = "Mat::create does not always allocate a new matrix."]
+    #[link_name = "\u{1}_ZNK2cv3Mat12isContinuousEv"]
+    pub fn cv_Mat_isContinuous(this: *const cv_Mat) -> bool;
+}
+extern "C" {
+    #[doc = "! returns true if the matrix is a submatrix of another matrix"]
+    #[link_name = "\u{1}_ZNK2cv3Mat11isSubmatrixEv"]
+    pub fn cv_Mat_isSubmatrix(this: *const cv_Mat) -> bool;
+}
+extern "C" {
+    #[doc = " @brief Returns the matrix element size in bytes."]
+    #[doc = ""]
+    #[doc = "The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 ,"]
+    #[doc = "the method returns 3\\*sizeof(short) or 6."]
+    #[link_name = "\u{1}_ZNK2cv3Mat8elemSizeEv"]
+    pub fn cv_Mat_elemSize(this: *const cv_Mat) -> size_t;
+}
+extern "C" {
+    #[doc = " @brief Returns the size of each matrix element channel in bytes."]
+    #[doc = ""]
+    #[doc = "The method returns the matrix element channel size in bytes, that is, it ignores the number of"]
+    #[doc = "channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2."]
+    #[link_name = "\u{1}_ZNK2cv3Mat9elemSize1Ev"]
+    pub fn cv_Mat_elemSize1(this: *const cv_Mat) -> size_t;
+}
+extern "C" {
+    #[doc = " @brief Returns the type of a matrix element."]
+    #[doc = ""]
+    #[doc = "The method returns a matrix element type. This is an identifier compatible with the CvMat type"]
+    #[doc = "system, like CV_16SC3 or 16-bit signed 3-channel array, and so on."]
+    #[link_name = "\u{1}_ZNK2cv3Mat4typeEv"]
+    pub fn cv_Mat_type(this: *const cv_Mat) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = " @brief Returns the depth of a matrix element."]
+    #[doc = ""]
+    #[doc = "The method returns the identifier of the matrix element depth (the type of each individual channel)."]
+    #[doc = "For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of"]
+    #[doc = "matrix types contains the following values:"]
+    #[doc = "-   CV_8U - 8-bit unsigned integers ( 0..255 )"]
+    #[doc = "-   CV_8S - 8-bit signed integers ( -128..127 )"]
+    #[doc = "-   CV_16U - 16-bit unsigned integers ( 0..65535 )"]
+    #[doc = "-   CV_16S - 16-bit signed integers ( -32768..32767 )"]
+    #[doc = "-   CV_32S - 32-bit signed integers ( -2147483648..2147483647 )"]
+    #[doc = "-   CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )"]
+    #[doc = "-   CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )"]
+    #[link_name = "\u{1}_ZNK2cv3Mat5depthEv"]
+    pub fn cv_Mat_depth(this: *const cv_Mat) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = " @brief Returns the number of matrix channels."]
+    #[doc = ""]
+    #[doc = "The method returns the number of matrix channels."]
+    #[link_name = "\u{1}_ZNK2cv3Mat8channelsEv"]
+    pub fn cv_Mat_channels(this: *const cv_Mat) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = " @brief Returns a normalized step."]
+    #[doc = ""]
+    #[doc = "The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an"]
+    #[doc = "arbitrary matrix element."]
+    #[link_name = "\u{1}_ZNK2cv3Mat5step1Ei"]
+    pub fn cv_Mat_step1(this: *const cv_Mat, i: ::std::os::raw::c_int) -> size_t;
+}
+extern "C" {
+    #[doc = " @brief Returns true if the array has no elements."]
+    #[doc = ""]
+    #[doc = "The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and"]
+    #[doc = "resize() methods `M.total() == 0` does not imply that `M.data == NULL`."]
+    #[link_name = "\u{1}_ZNK2cv3Mat5emptyEv"]
+    pub fn cv_Mat_empty(this: *const cv_Mat) -> bool;
+}
+extern "C" {
+    #[doc = " @brief Returns the total number of array elements."]
+    #[doc = ""]
+    #[doc = "The method returns the number of array elements (a number of pixels if the array represents an"]
+    #[doc = "image)."]
+    #[link_name = "\u{1}_ZNK2cv3Mat5totalEv"]
+    pub fn cv_Mat_total(this: *const cv_Mat) -> size_t;
+}
+extern "C" {
+    #[doc = " @brief Returns the total number of array elements."]
+    #[doc = ""]
+    #[doc = "The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim"]
+    #[link_name = "\u{1}_ZNK2cv3Mat5totalEii"]
+    pub fn cv_Mat_total1(
+        this: *const cv_Mat,
+        startDim: ::std::os::raw::c_int,
+        endDim: ::std::os::raw::c_int,
+    ) -> size_t;
+}
+extern "C" {
+    #[doc = " @param elemChannels Number of channels or number of columns the matrix should have."]
+    #[doc = "                     For a 2-D matrix, when the matrix has only 1 column, then it should have"]
+    #[doc = "                     elemChannels channels; When the matrix has only 1 channel,"]
+    #[doc = "                     then it should have elemChannels columns."]
+    #[doc = "                     For a 3-D matrix, it should have only one channel. Furthermore,"]
+    #[doc = "                     if the number of planes is not one, then the number of rows"]
+    #[doc = "                     within every plane has to be 1; if the number of rows within"]
+    #[doc = "                     every plane is not 1, then the number of planes has to be 1."]
+    #[doc = " @param depth The depth the matrix should have. Set it to -1 when any depth is fine."]
+    #[doc = " @param requireContinuous Set it to true to require the matrix to be continuous"]
+    #[doc = " @return -1 if the requirement is not satisfied."]
+    #[doc = "         Otherwise, it returns the number of elements in the matrix. Note"]
+    #[doc = "         that an element may have multiple channels."]
+    #[doc = ""]
+    #[doc = " The following code demonstrates its usage for a 2-d matrix:"]
+    #[doc = " @snippet snippets/core_mat_checkVector.cpp example-2d"]
+    #[doc = ""]
+    #[doc = " The following code demonstrates its usage for a 3-d matrix:"]
+    #[doc = " @snippet snippets/core_mat_checkVector.cpp example-3d"]
+    #[link_name = "\u{1}_ZNK2cv3Mat11checkVectorEiib"]
+    pub fn cv_Mat_checkVector(
+        this: *const cv_Mat,
+        elemChannels: ::std::os::raw::c_int,
+        depth: ::std::os::raw::c_int,
+        requireContinuous: bool,
+    ) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = " @brief Returns a pointer to the specified matrix row."]
+    #[doc = ""]
+    #[doc = "The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in"]
+    #[doc = "Mat::isContinuous to know how to use these methods."]
+    #[doc = "@param i0 A 0-based row index."]
+    #[link_name = "\u{1}_ZN2cv3Mat3ptrEi"]
+    pub fn cv_Mat_ptr(this: *mut cv_Mat, i0: ::std::os::raw::c_int) -> *mut uchar;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[link_name = "\u{1}_ZNK2cv3Mat3ptrEi"]
+    pub fn cv_Mat_ptr1(this: *const cv_Mat, i0: ::std::os::raw::c_int) -> *const uchar;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param row Index along the dimension 0"]
+    #[doc = "@param col Index along the dimension 1"]
+    #[link_name = "\u{1}_ZN2cv3Mat3ptrEii"]
+    pub fn cv_Mat_ptr2(
+        this: *mut cv_Mat,
+        row: ::std::os::raw::c_int,
+        col: ::std::os::raw::c_int,
+    ) -> *mut uchar;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param row Index along the dimension 0"]
+    #[doc = "@param col Index along the dimension 1"]
+    #[link_name = "\u{1}_ZNK2cv3Mat3ptrEii"]
+    pub fn cv_Mat_ptr3(
+        this: *const cv_Mat,
+        row: ::std::os::raw::c_int,
+        col: ::std::os::raw::c_int,
+    ) -> *const uchar;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[link_name = "\u{1}_ZN2cv3Mat3ptrEiii"]
+    pub fn cv_Mat_ptr4(
+        this: *mut cv_Mat,
+        i0: ::std::os::raw::c_int,
+        i1: ::std::os::raw::c_int,
+        i2: ::std::os::raw::c_int,
+    ) -> *mut uchar;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[link_name = "\u{1}_ZNK2cv3Mat3ptrEiii"]
+    pub fn cv_Mat_ptr5(
+        this: *const cv_Mat,
+        i0: ::std::os::raw::c_int,
+        i1: ::std::os::raw::c_int,
+        i2: ::std::os::raw::c_int,
+    ) -> *const uchar;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[link_name = "\u{1}_ZN2cv3Mat3ptrEPKi"]
+    pub fn cv_Mat_ptr6(this: *mut cv_Mat, idx: *const ::std::os::raw::c_int) -> *mut uchar;
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[link_name = "\u{1}_ZNK2cv3Mat3ptrEPKi"]
+    pub fn cv_Mat_ptr7(this: *const cv_Mat, idx: *const ::std::os::raw::c_int) -> *const uchar;
+}
+extern "C" {
+    #[doc = "! and the standard allocator"]
+    #[link_name = "\u{1}_ZN2cv3Mat15getStdAllocatorEv"]
+    pub fn cv_Mat_getStdAllocator() -> *mut cv_MatAllocator;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv3Mat19getDefaultAllocatorEv"]
+    pub fn cv_Mat_getDefaultAllocator() -> *mut cv_MatAllocator;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv3Mat19setDefaultAllocatorEPNS_12MatAllocatorE"]
+    pub fn cv_Mat_setDefaultAllocator(allocator: *mut cv_MatAllocator);
+}
+extern "C" {
+    #[doc = "! internal use method: updates the continuity flag"]
+    #[link_name = "\u{1}_ZN2cv3Mat20updateContinuityFlagEv"]
+    pub fn cv_Mat_updateContinuityFlag(this: *mut cv_Mat);
+}
+extern "C" {
+    #[doc = "These are various constructors that form a matrix. As noted in the AutomaticAllocation, often"]
+    #[doc = "the default constructor is enough, and the proper matrix will be allocated by an OpenCV function."]
+    #[doc = "The constructed matrix can further be assigned to another matrix or matrix expression or can be"]
+    #[doc = "allocated with Mat::create . In the former case, the old content is de-referenced."]
+    #[link_name = "\u{1}_ZN2cv3MatC1Ev"]
+    pub fn cv_Mat_Mat(this: *mut cv_Mat);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param rows Number of rows in a 2D array."]
+    #[doc = "@param cols Number of columns in a 2D array."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[link_name = "\u{1}_ZN2cv3MatC1Eiii"]
+    pub fn cv_Mat_Mat1(
+        this: *mut cv_Mat,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the"]
+    #[doc = "number of columns go in the reverse order."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ENS_5Size_IiEEi"]
+    pub fn cv_Mat_Mat2(this: *mut cv_Mat, size: cv_Size, type_: ::std::os::raw::c_int);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param rows Number of rows in a 2D array."]
+    #[doc = "@param cols Number of columns in a 2D array."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param s An optional value to initialize each matrix element with. To set all the matrix elements to"]
+    #[doc = "the particular value after the construction, use the assignment operator"]
+    #[doc = "Mat::operator=(const Scalar& value) ."]
+    #[link_name = "\u{1}_ZN2cv3MatC1EiiiRKNS_7Scalar_IdEE"]
+    pub fn cv_Mat_Mat3(
+        this: *mut cv_Mat,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the"]
+    #[doc = "number of columns go in the reverse order."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param s An optional value to initialize each matrix element with. To set all the matrix elements to"]
+    #[doc = "the particular value after the construction, use the assignment operator"]
+    #[doc = "Mat::operator=(const Scalar& value) ."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ENS_5Size_IiEEiRKNS_7Scalar_IdEE"]
+    pub fn cv_Mat_Mat4(
+        this: *mut cv_Mat,
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param ndims Array dimensionality."]
+    #[doc = "@param sizes Array of integers specifying an n-dimensional array shape."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[link_name = "\u{1}_ZN2cv3MatC1EiPKii"]
+    pub fn cv_Mat_Mat5(
+        this: *mut cv_Mat,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param sizes Array of integers specifying an n-dimensional array shape."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKSt6vectorIiSaIiEEi"]
+    pub fn cv_Mat_Mat6(
+        this: *mut cv_Mat,
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param ndims Array dimensionality."]
+    #[doc = "@param sizes Array of integers specifying an n-dimensional array shape."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param s An optional value to initialize each matrix element with. To set all the matrix elements to"]
+    #[doc = "the particular value after the construction, use the assignment operator"]
+    #[doc = "Mat::operator=(const Scalar& value) ."]
+    #[link_name = "\u{1}_ZN2cv3MatC1EiPKiiRKNS_7Scalar_IdEE"]
+    pub fn cv_Mat_Mat7(
+        this: *mut cv_Mat,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param sizes Array of integers specifying an n-dimensional array shape."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param s An optional value to initialize each matrix element with. To set all the matrix elements to"]
+    #[doc = "the particular value after the construction, use the assignment operator"]
+    #[doc = "Mat::operator=(const Scalar& value) ."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKSt6vectorIiSaIiEEiRKNS_7Scalar_IdEE"]
+    pub fn cv_Mat_Mat8(
+        this: *mut cv_Mat,
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied"]
+    #[doc = "by these constructors. Instead, the header pointing to m data or its sub-array is constructed and"]
+    #[doc = "associated with it. The reference counter, if any, is incremented. So, when you modify the matrix"]
+    #[doc = "formed using such a constructor, you also modify the corresponding elements of m . If you want to"]
+    #[doc = "have an independent copy of the sub-array, use Mat::clone() ."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKS0_"]
+    pub fn cv_Mat_Mat9(this: *mut cv_Mat, m: *const cv_Mat);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param rows Number of rows in a 2D array."]
+    #[doc = "@param cols Number of columns in a 2D array."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param data Pointer to the user data. Matrix constructors that take data and step parameters do not"]
+    #[doc = "allocate matrix data. Instead, they just initialize the matrix header that points to the specified"]
+    #[doc = "data, which means that no data is copied. This operation is very efficient and can be used to"]
+    #[doc = "process external data using OpenCV functions. The external data is not automatically deallocated, so"]
+    #[doc = "you should take care of it."]
+    #[doc = "@param step Number of bytes each matrix row occupies. The value should include the padding bytes at"]
+    #[doc = "the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed"]
+    #[doc = "and the actual step is calculated as cols*elemSize(). See Mat::elemSize."]
+    #[link_name = "\u{1}_ZN2cv3MatC1EiiiPvm"]
+    pub fn cv_Mat_Mat10(
+        this: *mut cv_Mat,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        step: size_t,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the"]
+    #[doc = "number of columns go in the reverse order."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param data Pointer to the user data. Matrix constructors that take data and step parameters do not"]
+    #[doc = "allocate matrix data. Instead, they just initialize the matrix header that points to the specified"]
+    #[doc = "data, which means that no data is copied. This operation is very efficient and can be used to"]
+    #[doc = "process external data using OpenCV functions. The external data is not automatically deallocated, so"]
+    #[doc = "you should take care of it."]
+    #[doc = "@param step Number of bytes each matrix row occupies. The value should include the padding bytes at"]
+    #[doc = "the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed"]
+    #[doc = "and the actual step is calculated as cols*elemSize(). See Mat::elemSize."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ENS_5Size_IiEEiPvm"]
+    pub fn cv_Mat_Mat11(
+        this: *mut cv_Mat,
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        step: size_t,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param ndims Array dimensionality."]
+    #[doc = "@param sizes Array of integers specifying an n-dimensional array shape."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param data Pointer to the user data. Matrix constructors that take data and step parameters do not"]
+    #[doc = "allocate matrix data. Instead, they just initialize the matrix header that points to the specified"]
+    #[doc = "data, which means that no data is copied. This operation is very efficient and can be used to"]
+    #[doc = "process external data using OpenCV functions. The external data is not automatically deallocated, so"]
+    #[doc = "you should take care of it."]
+    #[doc = "@param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always"]
+    #[doc = "set to the element size). If not specified, the matrix is assumed to be continuous."]
+    #[link_name = "\u{1}_ZN2cv3MatC1EiPKiiPvPKm"]
+    pub fn cv_Mat_Mat12(
+        this: *mut cv_Mat,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        steps: *const size_t,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param sizes Array of integers specifying an n-dimensional array shape."]
+    #[doc = "@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or"]
+    #[doc = "CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices."]
+    #[doc = "@param data Pointer to the user data. Matrix constructors that take data and step parameters do not"]
+    #[doc = "allocate matrix data. Instead, they just initialize the matrix header that points to the specified"]
+    #[doc = "data, which means that no data is copied. This operation is very efficient and can be used to"]
+    #[doc = "process external data using OpenCV functions. The external data is not automatically deallocated, so"]
+    #[doc = "you should take care of it."]
+    #[doc = "@param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always"]
+    #[doc = "set to the element size). If not specified, the matrix is assumed to be continuous."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKSt6vectorIiSaIiEEiPvPKm"]
+    pub fn cv_Mat_Mat13(
+        this: *mut cv_Mat,
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        steps: *const size_t,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied"]
+    #[doc = "by these constructors. Instead, the header pointing to m data or its sub-array is constructed and"]
+    #[doc = "associated with it. The reference counter, if any, is incremented. So, when you modify the matrix"]
+    #[doc = "formed using such a constructor, you also modify the corresponding elements of m . If you want to"]
+    #[doc = "have an independent copy of the sub-array, use Mat::clone() ."]
+    #[doc = "@param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range"]
+    #[doc = "end is exclusive. Use Range::all() to take all the rows."]
+    #[doc = "@param colRange Range of the m columns to take. Use Range::all() to take all the columns."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKS0_RKNS_5RangeES5_"]
+    pub fn cv_Mat_Mat14(
+        this: *mut cv_Mat,
+        m: *const cv_Mat,
+        rowRange: *const cv_Range,
+        colRange: *const cv_Range,
+    );
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied"]
+    #[doc = "by these constructors. Instead, the header pointing to m data or its sub-array is constructed and"]
+    #[doc = "associated with it. The reference counter, if any, is incremented. So, when you modify the matrix"]
+    #[doc = "formed using such a constructor, you also modify the corresponding elements of m . If you want to"]
+    #[doc = "have an independent copy of the sub-array, use Mat::clone() ."]
+    #[doc = "@param roi Region of interest."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKS0_RKNS_5Rect_IiEE"]
+    pub fn cv_Mat_Mat15(this: *mut cv_Mat, m: *const cv_Mat, roi: *const cv_Rect);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied"]
+    #[doc = "by these constructors. Instead, the header pointing to m data or its sub-array is constructed and"]
+    #[doc = "associated with it. The reference counter, if any, is incremented. So, when you modify the matrix"]
+    #[doc = "formed using such a constructor, you also modify the corresponding elements of m . If you want to"]
+    #[doc = "have an independent copy of the sub-array, use Mat::clone() ."]
+    #[doc = "@param ranges Array of selected ranges of m along each dimensionality."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKS0_PKNS_5RangeE"]
+    pub fn cv_Mat_Mat16(this: *mut cv_Mat, m: *const cv_Mat, ranges: *const cv_Range);
+}
+extern "C" {
+    #[doc = " @overload"]
+    #[doc = "@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied"]
+    #[doc = "by these constructors. Instead, the header pointing to m data or its sub-array is constructed and"]
+    #[doc = "associated with it. The reference counter, if any, is incremented. So, when you modify the matrix"]
+    #[doc = "formed using such a constructor, you also modify the corresponding elements of m . If you want to"]
+    #[doc = "have an independent copy of the sub-array, use Mat::clone() ."]
+    #[doc = "@param ranges Array of selected ranges of m along each dimensionality."]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKS0_RKSt6vectorINS_5RangeESaIS4_EE"]
+    pub fn cv_Mat_Mat17(this: *mut cv_Mat, m: *const cv_Mat, ranges: *const [u64; 3usize]);
+}
+extern "C" {
+    #[doc = "! download data from GpuMat"]
+    #[link_name = "\u{1}_ZN2cv3MatC1ERKNS_4cuda6GpuMatE"]
+    pub fn cv_Mat_Mat18(this: *mut cv_Mat, m: *const cv_cuda_GpuMat);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv3MatC1EOS0_"]
+    pub fn cv_Mat_Mat19(this: *mut cv_Mat, m: *mut cv_Mat);
+}
+extern "C" {
+    #[doc = "! destructor - calls release()"]
+    #[link_name = "\u{1}_ZN2cv3MatD1Ev"]
+    pub fn cv_Mat_Mat_destructor(this: *mut cv_Mat);
+}
+impl cv_Mat {
+    #[inline]
+    pub unsafe fn getUMat(
+        &self,
+        accessFlags: cv_AccessFlag,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_Mat_getUMat(self, accessFlags, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn row(&self, y: ::std::os::raw::c_int) -> cv_Mat {
+        cv_Mat_row(self, y)
+    }
+    #[inline]
+    pub unsafe fn col(&self, x: ::std::os::raw::c_int) -> cv_Mat {
+        cv_Mat_col(self, x)
+    }
+    #[inline]
+    pub unsafe fn rowRange(
+        &self,
+        startrow: ::std::os::raw::c_int,
+        endrow: ::std::os::raw::c_int,
+    ) -> cv_Mat {
+        cv_Mat_rowRange(self, startrow, endrow)
+    }
+    #[inline]
+    pub unsafe fn rowRange1(&self, r: *const cv_Range) -> cv_Mat {
+        cv_Mat_rowRange1(self, r)
+    }
+    #[inline]
+    pub unsafe fn colRange(
+        &self,
+        startcol: ::std::os::raw::c_int,
+        endcol: ::std::os::raw::c_int,
+    ) -> cv_Mat {
+        cv_Mat_colRange(self, startcol, endcol)
+    }
+    #[inline]
+    pub unsafe fn colRange1(&self, r: *const cv_Range) -> cv_Mat {
+        cv_Mat_colRange1(self, r)
+    }
+    #[inline]
+    pub unsafe fn diag(&self, d: ::std::os::raw::c_int) -> cv_Mat {
+        cv_Mat_diag(self, d)
+    }
+    #[inline]
+    pub unsafe fn diag1(d: *const cv_Mat) -> cv_Mat {
+        cv_Mat_diag1(d)
+    }
+    #[inline]
+    pub unsafe fn clone(&self) -> cv_Mat {
+        cv_Mat_clone(self)
+    }
+    #[inline]
+    pub unsafe fn copyTo(&self, m: cv_OutputArray) {
+        cv_Mat_copyTo(self, m)
+    }
+    #[inline]
+    pub unsafe fn copyTo1(&self, m: cv_OutputArray, mask: cv_InputArray) {
+        cv_Mat_copyTo1(self, m, mask)
+    }
+    #[inline]
+    pub unsafe fn convertTo(
+        &self,
+        m: cv_OutputArray,
+        rtype: ::std::os::raw::c_int,
+        alpha: f64,
+        beta: f64,
+    ) {
+        cv_Mat_convertTo(self, m, rtype, alpha, beta)
+    }
+    #[inline]
+    pub unsafe fn assignTo(&self, m: *mut cv_Mat, type_: ::std::os::raw::c_int) {
+        cv_Mat_assignTo(self, m, type_)
+    }
+    #[inline]
+    pub unsafe fn setTo(&mut self, value: cv_InputArray, mask: cv_InputArray) -> *mut cv_Mat {
+        cv_Mat_setTo(self, value, mask)
+    }
+    #[inline]
+    pub unsafe fn reshape(&self, cn: ::std::os::raw::c_int, rows: ::std::os::raw::c_int) -> cv_Mat {
+        cv_Mat_reshape(self, cn, rows)
+    }
+    #[inline]
+    pub unsafe fn reshape1(
+        &self,
+        cn: ::std::os::raw::c_int,
+        newndims: ::std::os::raw::c_int,
+        newsz: *const ::std::os::raw::c_int,
+    ) -> cv_Mat {
+        cv_Mat_reshape1(self, cn, newndims, newsz)
+    }
+    #[inline]
+    pub unsafe fn reshape2(
+        &self,
+        cn: ::std::os::raw::c_int,
+        newshape: *const [u64; 3usize],
+    ) -> cv_Mat {
+        cv_Mat_reshape2(self, cn, newshape)
+    }
+    #[inline]
+    pub unsafe fn t(&self) -> cv_MatExpr {
+        cv_Mat_t(self)
+    }
+    #[inline]
+    pub unsafe fn inv(&self, method: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_Mat_inv(self, method)
+    }
+    #[inline]
+    pub unsafe fn mul(&self, m: cv_InputArray, scale: f64) -> cv_MatExpr {
+        cv_Mat_mul(self, m, scale)
+    }
+    #[inline]
+    pub unsafe fn cross(&self, m: cv_InputArray) -> cv_Mat {
+        cv_Mat_cross(self, m)
+    }
+    #[inline]
+    pub unsafe fn dot(&self, m: cv_InputArray) -> f64 {
+        cv_Mat_dot(self, m)
+    }
+    #[inline]
+    pub unsafe fn zeros(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr {
+        cv_Mat_zeros(rows, cols, type_)
+    }
+    #[inline]
+    pub unsafe fn zeros1(size: cv_Size, type_: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_Mat_zeros1(size, type_)
+    }
+    #[inline]
+    pub unsafe fn zeros2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr {
+        cv_Mat_zeros2(ndims, sz, type_)
+    }
+    #[inline]
+    pub unsafe fn ones(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr {
+        cv_Mat_ones(rows, cols, type_)
+    }
+    #[inline]
+    pub unsafe fn ones1(size: cv_Size, type_: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_Mat_ones1(size, type_)
+    }
+    #[inline]
+    pub unsafe fn ones2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr {
+        cv_Mat_ones2(ndims, sz, type_)
+    }
+    #[inline]
+    pub unsafe fn eye(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> cv_MatExpr {
+        cv_Mat_eye(rows, cols, type_)
+    }
+    #[inline]
+    pub unsafe fn eye1(size: cv_Size, type_: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_Mat_eye1(size, type_)
+    }
+    #[inline]
+    pub unsafe fn create(
+        &mut self,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) {
+        cv_Mat_create(self, rows, cols, type_)
+    }
+    #[inline]
+    pub unsafe fn create1(&mut self, size: cv_Size, type_: ::std::os::raw::c_int) {
+        cv_Mat_create1(self, size, type_)
+    }
+    #[inline]
+    pub unsafe fn create2(
+        &mut self,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) {
+        cv_Mat_create2(self, ndims, sizes, type_)
+    }
+    #[inline]
+    pub unsafe fn create3(&mut self, sizes: *const [u64; 3usize], type_: ::std::os::raw::c_int) {
+        cv_Mat_create3(self, sizes, type_)
+    }
+    #[inline]
+    pub unsafe fn addref(&mut self) {
+        cv_Mat_addref(self)
+    }
+    #[inline]
+    pub unsafe fn release(&mut self) {
+        cv_Mat_release(self)
+    }
+    #[inline]
+    pub unsafe fn deallocate(&mut self) {
+        cv_Mat_deallocate(self)
+    }
+    #[inline]
+    pub unsafe fn copySize(&mut self, m: *const cv_Mat) {
+        cv_Mat_copySize(self, m)
+    }
+    #[inline]
+    pub unsafe fn reserve(&mut self, sz: size_t) {
+        cv_Mat_reserve(self, sz)
+    }
+    #[inline]
+    pub unsafe fn reserveBuffer(&mut self, sz: size_t) {
+        cv_Mat_reserveBuffer(self, sz)
+    }
+    #[inline]
+    pub unsafe fn resize(&mut self, sz: size_t) {
+        cv_Mat_resize(self, sz)
+    }
+    #[inline]
+    pub unsafe fn resize1(&mut self, sz: size_t, s: *const cv_Scalar) {
+        cv_Mat_resize1(self, sz, s)
+    }
+    #[inline]
+    pub unsafe fn push_back_(&mut self, elem: *const ::std::os::raw::c_void) {
+        cv_Mat_push_back_(self, elem)
+    }
+    #[inline]
+    pub unsafe fn push_back(&mut self, m: *const cv_Mat) {
+        cv_Mat_push_back(self, m)
+    }
+    #[inline]
+    pub unsafe fn pop_back(&mut self, nelems: size_t) {
+        cv_Mat_pop_back(self, nelems)
+    }
+    #[inline]
+    pub unsafe fn locateROI(&self, wholeSize: *mut cv_Size, ofs: *mut cv_Point) {
+        cv_Mat_locateROI(self, wholeSize, ofs)
+    }
+    #[inline]
+    pub unsafe fn adjustROI(
+        &mut self,
+        dtop: ::std::os::raw::c_int,
+        dbottom: ::std::os::raw::c_int,
+        dleft: ::std::os::raw::c_int,
+        dright: ::std::os::raw::c_int,
+    ) -> *mut cv_Mat {
+        cv_Mat_adjustROI(self, dtop, dbottom, dleft, dright)
+    }
+    #[inline]
+    pub unsafe fn isContinuous(&self) -> bool {
+        cv_Mat_isContinuous(self)
+    }
+    #[inline]
+    pub unsafe fn isSubmatrix(&self) -> bool {
+        cv_Mat_isSubmatrix(self)
+    }
+    #[inline]
+    pub unsafe fn elemSize(&self) -> size_t {
+        cv_Mat_elemSize(self)
+    }
+    #[inline]
+    pub unsafe fn elemSize1(&self) -> size_t {
+        cv_Mat_elemSize1(self)
+    }
+    #[inline]
+    pub unsafe fn type_(&self) -> ::std::os::raw::c_int {
+        cv_Mat_type(self)
+    }
+    #[inline]
+    pub unsafe fn depth(&self) -> ::std::os::raw::c_int {
+        cv_Mat_depth(self)
+    }
+    #[inline]
+    pub unsafe fn channels(&self) -> ::std::os::raw::c_int {
+        cv_Mat_channels(self)
+    }
+    #[inline]
+    pub unsafe fn step1(&self, i: ::std::os::raw::c_int) -> size_t {
+        cv_Mat_step1(self, i)
+    }
+    #[inline]
+    pub unsafe fn empty(&self) -> bool {
+        cv_Mat_empty(self)
+    }
+    #[inline]
+    pub unsafe fn total(&self) -> size_t {
+        cv_Mat_total(self)
+    }
+    #[inline]
+    pub unsafe fn total1(
+        &self,
+        startDim: ::std::os::raw::c_int,
+        endDim: ::std::os::raw::c_int,
+    ) -> size_t {
+        cv_Mat_total1(self, startDim, endDim)
+    }
+    #[inline]
+    pub unsafe fn checkVector(
+        &self,
+        elemChannels: ::std::os::raw::c_int,
+        depth: ::std::os::raw::c_int,
+        requireContinuous: bool,
+    ) -> ::std::os::raw::c_int {
+        cv_Mat_checkVector(self, elemChannels, depth, requireContinuous)
+    }
+    #[inline]
+    pub unsafe fn ptr(&mut self, i0: ::std::os::raw::c_int) -> *mut uchar {
+        cv_Mat_ptr(self, i0)
+    }
+    #[inline]
+    pub unsafe fn ptr1(&self, i0: ::std::os::raw::c_int) -> *const uchar {
+        cv_Mat_ptr1(self, i0)
+    }
+    #[inline]
+    pub unsafe fn ptr2(
+        &mut self,
+        row: ::std::os::raw::c_int,
+        col: ::std::os::raw::c_int,
+    ) -> *mut uchar {
+        cv_Mat_ptr2(self, row, col)
+    }
+    #[inline]
+    pub unsafe fn ptr3(
+        &self,
+        row: ::std::os::raw::c_int,
+        col: ::std::os::raw::c_int,
+    ) -> *const uchar {
+        cv_Mat_ptr3(self, row, col)
+    }
+    #[inline]
+    pub unsafe fn ptr4(
+        &mut self,
+        i0: ::std::os::raw::c_int,
+        i1: ::std::os::raw::c_int,
+        i2: ::std::os::raw::c_int,
+    ) -> *mut uchar {
+        cv_Mat_ptr4(self, i0, i1, i2)
+    }
+    #[inline]
+    pub unsafe fn ptr5(
+        &self,
+        i0: ::std::os::raw::c_int,
+        i1: ::std::os::raw::c_int,
+        i2: ::std::os::raw::c_int,
+    ) -> *const uchar {
+        cv_Mat_ptr5(self, i0, i1, i2)
+    }
+    #[inline]
+    pub unsafe fn ptr6(&mut self, idx: *const ::std::os::raw::c_int) -> *mut uchar {
+        cv_Mat_ptr6(self, idx)
+    }
+    #[inline]
+    pub unsafe fn ptr7(&self, idx: *const ::std::os::raw::c_int) -> *const uchar {
+        cv_Mat_ptr7(self, idx)
+    }
+    #[inline]
+    pub unsafe fn getStdAllocator() -> *mut cv_MatAllocator {
+        cv_Mat_getStdAllocator()
+    }
+    #[inline]
+    pub unsafe fn getDefaultAllocator() -> *mut cv_MatAllocator {
+        cv_Mat_getDefaultAllocator()
+    }
+    #[inline]
+    pub unsafe fn setDefaultAllocator(allocator: *mut cv_MatAllocator) {
+        cv_Mat_setDefaultAllocator(allocator)
+    }
+    #[inline]
+    pub unsafe fn updateContinuityFlag(&mut self) {
+        cv_Mat_updateContinuityFlag(self)
+    }
+    #[inline]
+    pub unsafe fn new() -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat(__bindgen_tmp.as_mut_ptr());
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new1(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat1(__bindgen_tmp.as_mut_ptr(), rows, cols, type_);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new2(size: cv_Size, type_: ::std::os::raw::c_int) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat2(__bindgen_tmp.as_mut_ptr(), size, type_);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new3(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat3(__bindgen_tmp.as_mut_ptr(), rows, cols, type_, s);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new4(size: cv_Size, type_: ::std::os::raw::c_int, s: *const cv_Scalar) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat4(__bindgen_tmp.as_mut_ptr(), size, type_, s);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new5(
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat5(__bindgen_tmp.as_mut_ptr(), ndims, sizes, type_);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new6(sizes: *const [u64; 3usize], type_: ::std::os::raw::c_int) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat6(__bindgen_tmp.as_mut_ptr(), sizes, type_);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new7(
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat7(__bindgen_tmp.as_mut_ptr(), ndims, sizes, type_, s);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new8(
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat8(__bindgen_tmp.as_mut_ptr(), sizes, type_, s);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new9(m: *const cv_Mat) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat9(__bindgen_tmp.as_mut_ptr(), m);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new10(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        step: size_t,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat10(__bindgen_tmp.as_mut_ptr(), rows, cols, type_, data, step);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new11(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        step: size_t,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat11(__bindgen_tmp.as_mut_ptr(), size, type_, data, step);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new12(
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        steps: *const size_t,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat12(__bindgen_tmp.as_mut_ptr(), ndims, sizes, type_, data, steps);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new13(
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+        data: *mut ::std::os::raw::c_void,
+        steps: *const size_t,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat13(__bindgen_tmp.as_mut_ptr(), sizes, type_, data, steps);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new14(
+        m: *const cv_Mat,
+        rowRange: *const cv_Range,
+        colRange: *const cv_Range,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat14(__bindgen_tmp.as_mut_ptr(), m, rowRange, colRange);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new15(m: *const cv_Mat, roi: *const cv_Rect) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat15(__bindgen_tmp.as_mut_ptr(), m, roi);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new16(m: *const cv_Mat, ranges: *const cv_Range) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat16(__bindgen_tmp.as_mut_ptr(), m, ranges);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new17(m: *const cv_Mat, ranges: *const [u64; 3usize]) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat17(__bindgen_tmp.as_mut_ptr(), m, ranges);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new18(m: *const cv_cuda_GpuMat) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat18(__bindgen_tmp.as_mut_ptr(), m);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new19(m: *mut cv_Mat) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_Mat_Mat19(__bindgen_tmp.as_mut_ptr(), m);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn destruct(&mut self) {
+        cv_Mat_Mat_destructor(self)
+    }
+}
+#[doc = " @todo document"]
+#[repr(C)]
+#[repr(align(8))]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_UMat {
+    pub _bindgen_opaque_blob: [u64; 10usize],
+}
+pub const cv_UMat_MAGIC_VAL: ::std::os::raw::c_uint = 1124007936;
+pub const cv_UMat_AUTO_STEP: ::std::os::raw::c_uint = 0;
+pub const cv_UMat_CONTINUOUS_FLAG: ::std::os::raw::c_uint = 16384;
+pub const cv_UMat_SUBMATRIX_FLAG: ::std::os::raw::c_uint = 32768;
+pub type cv_UMat__bindgen_ty_1 = ::std::os::raw::c_uint;
+pub const cv_UMat_MAGIC_MASK: ::std::os::raw::c_uint = 4294901760;
+pub const cv_UMat_TYPE_MASK: ::std::os::raw::c_uint = 4095;
+pub const cv_UMat_DEPTH_MASK: ::std::os::raw::c_uint = 7;
+pub type cv_UMat__bindgen_ty_2 = ::std::os::raw::c_uint;
+#[test]
+fn bindgen_test_layout_cv_UMat() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_UMat>(),
+        80usize,
+        concat!("Size of: ", stringify!(cv_UMat))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_UMat>(),
+        8usize,
+        concat!("Alignment of ", stringify!(cv_UMat))
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv4UMat6getMatENS_10AccessFlagE"]
+    pub fn cv_UMat_getMat(this: *const cv_UMat, flags: cv_AccessFlag) -> cv_Mat;
+}
+extern "C" {
+    #[doc = "! returns a new matrix header for the specified row"]
+    #[link_name = "\u{1}_ZNK2cv4UMat3rowEi"]
+    pub fn cv_UMat_row(this: *const cv_UMat, y: ::std::os::raw::c_int) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! returns a new matrix header for the specified column"]
+    #[link_name = "\u{1}_ZNK2cv4UMat3colEi"]
+    pub fn cv_UMat_col(this: *const cv_UMat, x: ::std::os::raw::c_int) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! ... for the specified row span"]
+    #[link_name = "\u{1}_ZNK2cv4UMat8rowRangeEii"]
+    pub fn cv_UMat_rowRange(
+        this: *const cv_UMat,
+        startrow: ::std::os::raw::c_int,
+        endrow: ::std::os::raw::c_int,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv4UMat8rowRangeERKNS_5RangeE"]
+    pub fn cv_UMat_rowRange1(this: *const cv_UMat, r: *const cv_Range) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! ... for the specified column span"]
+    #[link_name = "\u{1}_ZNK2cv4UMat8colRangeEii"]
+    pub fn cv_UMat_colRange(
+        this: *const cv_UMat,
+        startcol: ::std::os::raw::c_int,
+        endcol: ::std::os::raw::c_int,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv4UMat8colRangeERKNS_5RangeE"]
+    pub fn cv_UMat_colRange1(this: *const cv_UMat, r: *const cv_Range) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! ... for the specified diagonal"]
+    #[doc = "! (d=0 - the main diagonal,"]
+    #[doc = "!  >0 - a diagonal from the upper half,"]
+    #[doc = "!  <0 - a diagonal from the lower half)"]
+    #[link_name = "\u{1}_ZNK2cv4UMat4diagEi"]
+    pub fn cv_UMat_diag(this: *const cv_UMat, d: ::std::os::raw::c_int) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! constructs a square diagonal matrix which main diagonal is vector \"d\""]
+    #[link_name = "\u{1}_ZN2cv4UMat4diagERKS0_NS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_diag1(d: *const cv_UMat, usageFlags: cv_UMatUsageFlags) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! returns deep copy of the matrix, i.e. the data is copied"]
+    #[link_name = "\u{1}_ZNK2cv4UMat5cloneEv"]
+    pub fn cv_UMat_clone(this: *const cv_UMat) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! copies the matrix content to \"m\"."]
+    #[link_name = "\u{1}_ZNK2cv4UMat6copyToERKNS_12_OutputArrayE"]
+    pub fn cv_UMat_copyTo(this: *const cv_UMat, m: cv_OutputArray);
+}
+extern "C" {
+    #[doc = "! copies those matrix elements to \"m\" that are marked with non-zero mask elements."]
+    #[link_name = "\u{1}_ZNK2cv4UMat6copyToERKNS_12_OutputArrayERKNS_11_InputArrayE"]
+    pub fn cv_UMat_copyTo1(this: *const cv_UMat, m: cv_OutputArray, mask: cv_InputArray);
+}
+extern "C" {
+    #[doc = "! converts matrix to another datatype with optional scaling. See cvConvertScale."]
+    #[link_name = "\u{1}_ZNK2cv4UMat9convertToERKNS_12_OutputArrayEidd"]
+    pub fn cv_UMat_convertTo(
+        this: *const cv_UMat,
+        m: cv_OutputArray,
+        rtype: ::std::os::raw::c_int,
+        alpha: f64,
+        beta: f64,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv4UMat8assignToERS0_i"]
+    pub fn cv_UMat_assignTo(this: *const cv_UMat, m: *mut cv_UMat, type_: ::std::os::raw::c_int);
+}
+extern "C" {
+    #[doc = "! sets some of the matrix elements to s, according to the mask"]
+    #[link_name = "\u{1}_ZN2cv4UMat5setToERKNS_11_InputArrayES3_"]
+    pub fn cv_UMat_setTo(
+        this: *mut cv_UMat,
+        value: cv_InputArray,
+        mask: cv_InputArray,
+    ) -> *mut cv_UMat;
+}
+extern "C" {
+    #[doc = "! creates alternative matrix header for the same data, with different"]
+    #[link_name = "\u{1}_ZNK2cv4UMat7reshapeEii"]
+    pub fn cv_UMat_reshape(
+        this: *const cv_UMat,
+        cn: ::std::os::raw::c_int,
+        rows: ::std::os::raw::c_int,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv4UMat7reshapeEiiPKi"]
+    pub fn cv_UMat_reshape1(
+        this: *const cv_UMat,
+        cn: ::std::os::raw::c_int,
+        newndims: ::std::os::raw::c_int,
+        newsz: *const ::std::os::raw::c_int,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! matrix transposition by means of matrix expressions"]
+    #[link_name = "\u{1}_ZNK2cv4UMat1tEv"]
+    pub fn cv_UMat_t(this: *const cv_UMat) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! matrix inversion by means of matrix expressions"]
+    #[link_name = "\u{1}_ZNK2cv4UMat3invEi"]
+    pub fn cv_UMat_inv(this: *const cv_UMat, method: ::std::os::raw::c_int) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! per-element matrix multiplication by means of matrix expressions"]
+    #[link_name = "\u{1}_ZNK2cv4UMat3mulERKNS_11_InputArrayEd"]
+    pub fn cv_UMat_mul(this: *const cv_UMat, m: cv_InputArray, scale: f64) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! computes dot-product"]
+    #[link_name = "\u{1}_ZNK2cv4UMat3dotERKNS_11_InputArrayE"]
+    pub fn cv_UMat_dot(this: *const cv_UMat, m: cv_InputArray) -> f64;
+}
+extern "C" {
+    #[doc = "! Matlab-style matrix initialization"]
+    #[link_name = "\u{1}_ZN2cv4UMat5zerosEiiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_zeros(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat5zerosENS_5Size_IiEEiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_zeros1(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat5zerosEiPKiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_zeros2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat4onesEiiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_ones(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat4onesENS_5Size_IiEEiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_ones1(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat4onesEiPKiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_ones2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat3eyeEiiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_eye(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat3eyeENS_5Size_IiEEiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_eye1(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat;
+}
+extern "C" {
+    #[doc = "! allocates new matrix data unless the matrix already has specified size and type."]
+    #[link_name = "\u{1}_ZN2cv4UMat6createEiiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_create(
+        this: *mut cv_UMat,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat6createENS_5Size_IiEEiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_create1(
+        this: *mut cv_UMat,
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat6createEiPKiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_create2(
+        this: *mut cv_UMat,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMat6createERKSt6vectorIiSaIiEEiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_create3(
+        this: *mut cv_UMat,
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[doc = "! increases the reference counter; use with care to avoid memleaks"]
+    #[link_name = "\u{1}_ZN2cv4UMat6addrefEv"]
+    pub fn cv_UMat_addref(this: *mut cv_UMat);
+}
+extern "C" {
+    #[doc = "! decreases reference counter;"]
+    #[link_name = "\u{1}_ZN2cv4UMat7releaseEv"]
+    pub fn cv_UMat_release(this: *mut cv_UMat);
+}
+extern "C" {
+    #[doc = "! deallocates the matrix data"]
+    #[link_name = "\u{1}_ZN2cv4UMat10deallocateEv"]
+    pub fn cv_UMat_deallocate(this: *mut cv_UMat);
+}
+extern "C" {
+    #[doc = "! internal use function; properly re-allocates _size, _step arrays"]
+    #[link_name = "\u{1}_ZN2cv4UMat8copySizeERKS0_"]
+    pub fn cv_UMat_copySize(this: *mut cv_UMat, m: *const cv_UMat);
+}
+extern "C" {
+    #[doc = "! locates matrix header within a parent matrix. See below"]
+    #[link_name = "\u{1}_ZNK2cv4UMat9locateROIERNS_5Size_IiEERNS_6Point_IiEE"]
+    pub fn cv_UMat_locateROI(this: *const cv_UMat, wholeSize: *mut cv_Size, ofs: *mut cv_Point);
+}
+extern "C" {
+    #[doc = "! moves/resizes the current matrix ROI inside the parent matrix."]
+    #[link_name = "\u{1}_ZN2cv4UMat9adjustROIEiiii"]
+    pub fn cv_UMat_adjustROI(
+        this: *mut cv_UMat,
+        dtop: ::std::os::raw::c_int,
+        dbottom: ::std::os::raw::c_int,
+        dleft: ::std::os::raw::c_int,
+        dright: ::std::os::raw::c_int,
+    ) -> *mut cv_UMat;
+}
+extern "C" {
+    #[doc = "! returns true iff the matrix data is continuous"]
+    #[link_name = "\u{1}_ZNK2cv4UMat12isContinuousEv"]
+    pub fn cv_UMat_isContinuous(this: *const cv_UMat) -> bool;
+}
+extern "C" {
+    #[doc = "! returns true if the matrix is a submatrix of another matrix"]
+    #[link_name = "\u{1}_ZNK2cv4UMat11isSubmatrixEv"]
+    pub fn cv_UMat_isSubmatrix(this: *const cv_UMat) -> bool;
+}
+extern "C" {
+    #[doc = "! returns element size in bytes,"]
+    #[link_name = "\u{1}_ZNK2cv4UMat8elemSizeEv"]
+    pub fn cv_UMat_elemSize(this: *const cv_UMat) -> size_t;
+}
+extern "C" {
+    #[doc = "! returns the size of element channel in bytes."]
+    #[link_name = "\u{1}_ZNK2cv4UMat9elemSize1Ev"]
+    pub fn cv_UMat_elemSize1(this: *const cv_UMat) -> size_t;
+}
+extern "C" {
+    #[doc = "! returns element type, similar to CV_MAT_TYPE(cvmat->type)"]
+    #[link_name = "\u{1}_ZNK2cv4UMat4typeEv"]
+    pub fn cv_UMat_type(this: *const cv_UMat) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = "! returns element type, similar to CV_MAT_DEPTH(cvmat->type)"]
+    #[link_name = "\u{1}_ZNK2cv4UMat5depthEv"]
+    pub fn cv_UMat_depth(this: *const cv_UMat) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = "! returns element type, similar to CV_MAT_CN(cvmat->type)"]
+    #[link_name = "\u{1}_ZNK2cv4UMat8channelsEv"]
+    pub fn cv_UMat_channels(this: *const cv_UMat) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = "! returns step/elemSize1()"]
+    #[link_name = "\u{1}_ZNK2cv4UMat5step1Ei"]
+    pub fn cv_UMat_step1(this: *const cv_UMat, i: ::std::os::raw::c_int) -> size_t;
+}
+extern "C" {
+    #[doc = "! returns true if matrix data is NULL"]
+    #[link_name = "\u{1}_ZNK2cv4UMat5emptyEv"]
+    pub fn cv_UMat_empty(this: *const cv_UMat) -> bool;
+}
+extern "C" {
+    #[doc = "! returns the total number of matrix elements"]
+    #[link_name = "\u{1}_ZNK2cv4UMat5totalEv"]
+    pub fn cv_UMat_total(this: *const cv_UMat) -> size_t;
+}
+extern "C" {
+    #[doc = "! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise"]
+    #[link_name = "\u{1}_ZNK2cv4UMat11checkVectorEiib"]
+    pub fn cv_UMat_checkVector(
+        this: *const cv_UMat,
+        elemChannels: ::std::os::raw::c_int,
+        depth: ::std::os::raw::c_int,
+        requireContinuous: bool,
+    ) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[doc = " Returns the OpenCL buffer handle on which UMat operates on."]
+    #[doc = "The UMat instance should be kept alive during the use of the handle to prevent the buffer to be"]
+    #[doc = "returned to the OpenCV buffer pool."]
+    #[link_name = "\u{1}_ZNK2cv4UMat6handleENS_10AccessFlagE"]
+    pub fn cv_UMat_handle(
+        this: *const cv_UMat,
+        accessFlags: cv_AccessFlag,
+    ) -> *mut ::std::os::raw::c_void;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv4UMat8ndoffsetEPm"]
+    pub fn cv_UMat_ndoffset(this: *const cv_UMat, ofs: *mut size_t);
+}
+extern "C" {
+    #[doc = "! and the standard allocator"]
+    #[link_name = "\u{1}_ZN2cv4UMat15getStdAllocatorEv"]
+    pub fn cv_UMat_getStdAllocator() -> *mut cv_MatAllocator;
+}
+extern "C" {
+    #[doc = "! internal use method: updates the continuity flag"]
+    #[link_name = "\u{1}_ZN2cv4UMat20updateContinuityFlagEv"]
+    pub fn cv_UMat_updateContinuityFlag(this: *mut cv_UMat);
+}
+extern "C" {
+    #[doc = "! default constructor"]
+    #[link_name = "\u{1}_ZN2cv4UMatC1ENS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_UMat(this: *mut cv_UMat, usageFlags: cv_UMatUsageFlags);
+}
+extern "C" {
+    #[doc = "! constructs 2D matrix of the specified size and type"]
+    #[link_name = "\u{1}_ZN2cv4UMatC1EiiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_UMat1(
+        this: *mut cv_UMat,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMatC1ENS_5Size_IiEEiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_UMat2(
+        this: *mut cv_UMat,
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[doc = "! constructs 2D matrix and fills it with the specified value _s."]
+    #[link_name = "\u{1}_ZN2cv4UMatC1EiiiRKNS_7Scalar_IdEENS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_UMat3(
+        this: *mut cv_UMat,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMatC1ENS_5Size_IiEEiRKNS_7Scalar_IdEENS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_UMat4(
+        this: *mut cv_UMat,
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[doc = "! constructs n-dimensional matrix"]
+    #[link_name = "\u{1}_ZN2cv4UMatC1EiPKiiNS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_UMat5(
+        this: *mut cv_UMat,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMatC1EiPKiiRKNS_7Scalar_IdEENS_14UMatUsageFlagsE"]
+    pub fn cv_UMat_UMat6(
+        this: *mut cv_UMat,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+        usageFlags: cv_UMatUsageFlags,
+    );
+}
+extern "C" {
+    #[doc = "! copy constructor"]
+    #[link_name = "\u{1}_ZN2cv4UMatC1ERKS0_"]
+    pub fn cv_UMat_UMat7(this: *mut cv_UMat, m: *const cv_UMat);
+}
+extern "C" {
+    #[doc = "! creates a matrix header for a part of the bigger matrix"]
+    #[link_name = "\u{1}_ZN2cv4UMatC1ERKS0_RKNS_5RangeES5_"]
+    pub fn cv_UMat_UMat8(
+        this: *mut cv_UMat,
+        m: *const cv_UMat,
+        rowRange: *const cv_Range,
+        colRange: *const cv_Range,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMatC1ERKS0_RKNS_5Rect_IiEE"]
+    pub fn cv_UMat_UMat9(this: *mut cv_UMat, m: *const cv_UMat, roi: *const cv_Rect);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMatC1ERKS0_PKNS_5RangeE"]
+    pub fn cv_UMat_UMat10(this: *mut cv_UMat, m: *const cv_UMat, ranges: *const cv_Range);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMatC1ERKS0_RKSt6vectorINS_5RangeESaIS4_EE"]
+    pub fn cv_UMat_UMat11(this: *mut cv_UMat, m: *const cv_UMat, ranges: *const [u64; 3usize]);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv4UMatC1EOS0_"]
+    pub fn cv_UMat_UMat12(this: *mut cv_UMat, m: *mut cv_UMat);
+}
+extern "C" {
+    #[doc = "! destructor - calls release()"]
+    #[link_name = "\u{1}_ZN2cv4UMatD1Ev"]
+    pub fn cv_UMat_UMat_destructor(this: *mut cv_UMat);
+}
+impl cv_UMat {
+    #[inline]
+    pub unsafe fn getMat(&self, flags: cv_AccessFlag) -> cv_Mat {
+        cv_UMat_getMat(self, flags)
+    }
+    #[inline]
+    pub unsafe fn row(&self, y: ::std::os::raw::c_int) -> cv_UMat {
+        cv_UMat_row(self, y)
+    }
+    #[inline]
+    pub unsafe fn col(&self, x: ::std::os::raw::c_int) -> cv_UMat {
+        cv_UMat_col(self, x)
+    }
+    #[inline]
+    pub unsafe fn rowRange(
+        &self,
+        startrow: ::std::os::raw::c_int,
+        endrow: ::std::os::raw::c_int,
+    ) -> cv_UMat {
+        cv_UMat_rowRange(self, startrow, endrow)
+    }
+    #[inline]
+    pub unsafe fn rowRange1(&self, r: *const cv_Range) -> cv_UMat {
+        cv_UMat_rowRange1(self, r)
+    }
+    #[inline]
+    pub unsafe fn colRange(
+        &self,
+        startcol: ::std::os::raw::c_int,
+        endcol: ::std::os::raw::c_int,
+    ) -> cv_UMat {
+        cv_UMat_colRange(self, startcol, endcol)
+    }
+    #[inline]
+    pub unsafe fn colRange1(&self, r: *const cv_Range) -> cv_UMat {
+        cv_UMat_colRange1(self, r)
+    }
+    #[inline]
+    pub unsafe fn diag(&self, d: ::std::os::raw::c_int) -> cv_UMat {
+        cv_UMat_diag(self, d)
+    }
+    #[inline]
+    pub unsafe fn diag1(d: *const cv_UMat, usageFlags: cv_UMatUsageFlags) -> cv_UMat {
+        cv_UMat_diag1(d, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn clone(&self) -> cv_UMat {
+        cv_UMat_clone(self)
+    }
+    #[inline]
+    pub unsafe fn copyTo(&self, m: cv_OutputArray) {
+        cv_UMat_copyTo(self, m)
+    }
+    #[inline]
+    pub unsafe fn copyTo1(&self, m: cv_OutputArray, mask: cv_InputArray) {
+        cv_UMat_copyTo1(self, m, mask)
+    }
+    #[inline]
+    pub unsafe fn convertTo(
+        &self,
+        m: cv_OutputArray,
+        rtype: ::std::os::raw::c_int,
+        alpha: f64,
+        beta: f64,
+    ) {
+        cv_UMat_convertTo(self, m, rtype, alpha, beta)
+    }
+    #[inline]
+    pub unsafe fn assignTo(&self, m: *mut cv_UMat, type_: ::std::os::raw::c_int) {
+        cv_UMat_assignTo(self, m, type_)
+    }
+    #[inline]
+    pub unsafe fn setTo(&mut self, value: cv_InputArray, mask: cv_InputArray) -> *mut cv_UMat {
+        cv_UMat_setTo(self, value, mask)
+    }
+    #[inline]
+    pub unsafe fn reshape(
+        &self,
+        cn: ::std::os::raw::c_int,
+        rows: ::std::os::raw::c_int,
+    ) -> cv_UMat {
+        cv_UMat_reshape(self, cn, rows)
+    }
+    #[inline]
+    pub unsafe fn reshape1(
+        &self,
+        cn: ::std::os::raw::c_int,
+        newndims: ::std::os::raw::c_int,
+        newsz: *const ::std::os::raw::c_int,
+    ) -> cv_UMat {
+        cv_UMat_reshape1(self, cn, newndims, newsz)
+    }
+    #[inline]
+    pub unsafe fn t(&self) -> cv_UMat {
+        cv_UMat_t(self)
+    }
+    #[inline]
+    pub unsafe fn inv(&self, method: ::std::os::raw::c_int) -> cv_UMat {
+        cv_UMat_inv(self, method)
+    }
+    #[inline]
+    pub unsafe fn mul(&self, m: cv_InputArray, scale: f64) -> cv_UMat {
+        cv_UMat_mul(self, m, scale)
+    }
+    #[inline]
+    pub unsafe fn dot(&self, m: cv_InputArray) -> f64 {
+        cv_UMat_dot(self, m)
+    }
+    #[inline]
+    pub unsafe fn zeros(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_zeros(rows, cols, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn zeros1(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_zeros1(size, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn zeros2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_zeros2(ndims, sz, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn ones(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_ones(rows, cols, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn ones1(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_ones1(size, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn ones2(
+        ndims: ::std::os::raw::c_int,
+        sz: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_ones2(ndims, sz, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn eye(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_eye(rows, cols, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn eye1(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> cv_UMat {
+        cv_UMat_eye1(size, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn create(
+        &mut self,
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) {
+        cv_UMat_create(self, rows, cols, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn create1(
+        &mut self,
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) {
+        cv_UMat_create1(self, size, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn create2(
+        &mut self,
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) {
+        cv_UMat_create2(self, ndims, sizes, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn create3(
+        &mut self,
+        sizes: *const [u64; 3usize],
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) {
+        cv_UMat_create3(self, sizes, type_, usageFlags)
+    }
+    #[inline]
+    pub unsafe fn addref(&mut self) {
+        cv_UMat_addref(self)
+    }
+    #[inline]
+    pub unsafe fn release(&mut self) {
+        cv_UMat_release(self)
+    }
+    #[inline]
+    pub unsafe fn deallocate(&mut self) {
+        cv_UMat_deallocate(self)
+    }
+    #[inline]
+    pub unsafe fn copySize(&mut self, m: *const cv_UMat) {
+        cv_UMat_copySize(self, m)
+    }
+    #[inline]
+    pub unsafe fn locateROI(&self, wholeSize: *mut cv_Size, ofs: *mut cv_Point) {
+        cv_UMat_locateROI(self, wholeSize, ofs)
+    }
+    #[inline]
+    pub unsafe fn adjustROI(
+        &mut self,
+        dtop: ::std::os::raw::c_int,
+        dbottom: ::std::os::raw::c_int,
+        dleft: ::std::os::raw::c_int,
+        dright: ::std::os::raw::c_int,
+    ) -> *mut cv_UMat {
+        cv_UMat_adjustROI(self, dtop, dbottom, dleft, dright)
+    }
+    #[inline]
+    pub unsafe fn isContinuous(&self) -> bool {
+        cv_UMat_isContinuous(self)
+    }
+    #[inline]
+    pub unsafe fn isSubmatrix(&self) -> bool {
+        cv_UMat_isSubmatrix(self)
+    }
+    #[inline]
+    pub unsafe fn elemSize(&self) -> size_t {
+        cv_UMat_elemSize(self)
+    }
+    #[inline]
+    pub unsafe fn elemSize1(&self) -> size_t {
+        cv_UMat_elemSize1(self)
+    }
+    #[inline]
+    pub unsafe fn type_(&self) -> ::std::os::raw::c_int {
+        cv_UMat_type(self)
+    }
+    #[inline]
+    pub unsafe fn depth(&self) -> ::std::os::raw::c_int {
+        cv_UMat_depth(self)
+    }
+    #[inline]
+    pub unsafe fn channels(&self) -> ::std::os::raw::c_int {
+        cv_UMat_channels(self)
+    }
+    #[inline]
+    pub unsafe fn step1(&self, i: ::std::os::raw::c_int) -> size_t {
+        cv_UMat_step1(self, i)
+    }
+    #[inline]
+    pub unsafe fn empty(&self) -> bool {
+        cv_UMat_empty(self)
+    }
+    #[inline]
+    pub unsafe fn total(&self) -> size_t {
+        cv_UMat_total(self)
+    }
+    #[inline]
+    pub unsafe fn checkVector(
+        &self,
+        elemChannels: ::std::os::raw::c_int,
+        depth: ::std::os::raw::c_int,
+        requireContinuous: bool,
+    ) -> ::std::os::raw::c_int {
+        cv_UMat_checkVector(self, elemChannels, depth, requireContinuous)
+    }
+    #[inline]
+    pub unsafe fn handle(&self, accessFlags: cv_AccessFlag) -> *mut ::std::os::raw::c_void {
+        cv_UMat_handle(self, accessFlags)
+    }
+    #[inline]
+    pub unsafe fn ndoffset(&self, ofs: *mut size_t) {
+        cv_UMat_ndoffset(self, ofs)
+    }
+    #[inline]
+    pub unsafe fn getStdAllocator() -> *mut cv_MatAllocator {
+        cv_UMat_getStdAllocator()
+    }
+    #[inline]
+    pub unsafe fn updateContinuityFlag(&mut self) {
+        cv_UMat_updateContinuityFlag(self)
+    }
+    #[inline]
+    pub unsafe fn new(usageFlags: cv_UMatUsageFlags) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat(__bindgen_tmp.as_mut_ptr(), usageFlags);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new1(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat1(__bindgen_tmp.as_mut_ptr(), rows, cols, type_, usageFlags);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new2(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat2(__bindgen_tmp.as_mut_ptr(), size, type_, usageFlags);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new3(
+        rows: ::std::os::raw::c_int,
+        cols: ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat3(__bindgen_tmp.as_mut_ptr(), rows, cols, type_, s, usageFlags);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new4(
+        size: cv_Size,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat4(__bindgen_tmp.as_mut_ptr(), size, type_, s, usageFlags);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new5(
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat5(__bindgen_tmp.as_mut_ptr(), ndims, sizes, type_, usageFlags);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new6(
+        ndims: ::std::os::raw::c_int,
+        sizes: *const ::std::os::raw::c_int,
+        type_: ::std::os::raw::c_int,
+        s: *const cv_Scalar,
+        usageFlags: cv_UMatUsageFlags,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat6(
+            __bindgen_tmp.as_mut_ptr(),
+            ndims,
+            sizes,
+            type_,
+            s,
+            usageFlags,
+        );
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new7(m: *const cv_UMat) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat7(__bindgen_tmp.as_mut_ptr(), m);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new8(
+        m: *const cv_UMat,
+        rowRange: *const cv_Range,
+        colRange: *const cv_Range,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat8(__bindgen_tmp.as_mut_ptr(), m, rowRange, colRange);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new9(m: *const cv_UMat, roi: *const cv_Rect) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat9(__bindgen_tmp.as_mut_ptr(), m, roi);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new10(m: *const cv_UMat, ranges: *const cv_Range) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat10(__bindgen_tmp.as_mut_ptr(), m, ranges);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new11(m: *const cv_UMat, ranges: *const [u64; 3usize]) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat11(__bindgen_tmp.as_mut_ptr(), m, ranges);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new12(m: *mut cv_UMat) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_UMat_UMat12(__bindgen_tmp.as_mut_ptr(), m);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn destruct(&mut self) {
+        cv_UMat_UMat_destructor(self)
+    }
+}
+#[doc = " Matrix Expressions /////////////////////////////////"]
+#[repr(C)]
+#[repr(align(8))]
+#[derive(Debug, Copy, Clone)]
+pub struct cv_MatOp {
+    pub _bindgen_opaque_blob: u64,
+}
+#[test]
+fn bindgen_test_layout_cv_MatOp() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_MatOp>(),
+        8usize,
+        concat!("Size of: ", stringify!(cv_MatOp))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_MatOp>(),
+        8usize,
+        concat!("Alignment of ", stringify!(cv_MatOp))
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv5MatOpC2Ev"]
+    pub fn cv_MatOp_MatOp(this: *mut cv_MatOp);
+}
+impl cv_MatOp {
+    #[inline]
+    pub unsafe fn new() -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_MatOp_MatOp(__bindgen_tmp.as_mut_ptr());
+        __bindgen_tmp.assume_init()
+    }
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv5MatOpD1Ev"]
+    pub fn cv_MatOp_MatOp_destructor(this: *mut cv_MatOp);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp11elementWiseERKNS_7MatExprE"]
+    pub fn cv_MatOp_elementWise(this: *mut ::std::os::raw::c_void, expr: *const cv_MatExpr)
+        -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp3roiERKNS_7MatExprERKNS_5RangeES6_RS1_"]
+    pub fn cv_MatOp_roi(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        rowRange: *const cv_Range,
+        colRange: *const cv_Range,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp4diagERKNS_7MatExprEiRS1_"]
+    pub fn cv_MatOp_diag(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        d: ::std::os::raw::c_int,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp12augAssignAddERKNS_7MatExprERNS_3MatE"]
+    pub fn cv_MatOp_augAssignAdd(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        m: *mut cv_Mat,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp17augAssignSubtractERKNS_7MatExprERNS_3MatE"]
+    pub fn cv_MatOp_augAssignSubtract(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        m: *mut cv_Mat,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp17augAssignMultiplyERKNS_7MatExprERNS_3MatE"]
+    pub fn cv_MatOp_augAssignMultiply(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        m: *mut cv_Mat,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp15augAssignDivideERKNS_7MatExprERNS_3MatE"]
+    pub fn cv_MatOp_augAssignDivide(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        m: *mut cv_Mat,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp12augAssignAndERKNS_7MatExprERNS_3MatE"]
+    pub fn cv_MatOp_augAssignAnd(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        m: *mut cv_Mat,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp11augAssignOrERKNS_7MatExprERNS_3MatE"]
+    pub fn cv_MatOp_augAssignOr(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        m: *mut cv_Mat,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp12augAssignXorERKNS_7MatExprERNS_3MatE"]
+    pub fn cv_MatOp_augAssignXor(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        m: *mut cv_Mat,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp3addERKNS_7MatExprES3_RS1_"]
+    pub fn cv_MatOp_add(
+        this: *mut ::std::os::raw::c_void,
+        expr1: *const cv_MatExpr,
+        expr2: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp3addERKNS_7MatExprERKNS_7Scalar_IdEERS1_"]
+    pub fn cv_MatOp_add1(
+        this: *mut ::std::os::raw::c_void,
+        expr1: *const cv_MatExpr,
+        s: *const cv_Scalar,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp8subtractERKNS_7MatExprES3_RS1_"]
+    pub fn cv_MatOp_subtract(
+        this: *mut ::std::os::raw::c_void,
+        expr1: *const cv_MatExpr,
+        expr2: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp8subtractERKNS_7Scalar_IdEERKNS_7MatExprERS5_"]
+    pub fn cv_MatOp_subtract1(
+        this: *mut ::std::os::raw::c_void,
+        s: *const cv_Scalar,
+        expr: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp8multiplyERKNS_7MatExprES3_RS1_d"]
+    pub fn cv_MatOp_multiply(
+        this: *mut ::std::os::raw::c_void,
+        expr1: *const cv_MatExpr,
+        expr2: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+        scale: f64,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp8multiplyERKNS_7MatExprEdRS1_"]
+    pub fn cv_MatOp_multiply1(
+        this: *mut ::std::os::raw::c_void,
+        expr1: *const cv_MatExpr,
+        s: f64,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp6divideERKNS_7MatExprES3_RS1_d"]
+    pub fn cv_MatOp_divide(
+        this: *mut ::std::os::raw::c_void,
+        expr1: *const cv_MatExpr,
+        expr2: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+        scale: f64,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp6divideEdRKNS_7MatExprERS1_"]
+    pub fn cv_MatOp_divide1(
+        this: *mut ::std::os::raw::c_void,
+        s: f64,
+        expr: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp3absERKNS_7MatExprERS1_"]
+    pub fn cv_MatOp_abs(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp9transposeERKNS_7MatExprERS1_"]
+    pub fn cv_MatOp_transpose(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp6matmulERKNS_7MatExprES3_RS1_"]
+    pub fn cv_MatOp_matmul(
+        this: *mut ::std::os::raw::c_void,
+        expr1: *const cv_MatExpr,
+        expr2: *const cv_MatExpr,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp6invertERKNS_7MatExprEiRS1_"]
+    pub fn cv_MatOp_invert(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+        method: ::std::os::raw::c_int,
+        res: *mut cv_MatExpr,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp4sizeERKNS_7MatExprE"]
+    pub fn cv_MatOp_size(this: *mut ::std::os::raw::c_void, expr: *const cv_MatExpr) -> cv_Size;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv5MatOp4typeERKNS_7MatExprE"]
+    pub fn cv_MatOp_type(
+        this: *mut ::std::os::raw::c_void,
+        expr: *const cv_MatExpr,
+    ) -> ::std::os::raw::c_int;
+}
+#[doc = " @brief Matrix expression representation"]
+#[doc = "@anchor MatrixExpressions"]
+#[doc = "This is a list of implemented matrix operations that can be combined in arbitrary complex"]
+#[doc = "expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a"]
+#[doc = "real-valued scalar ( double )):"]
+#[doc = "-   Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A`"]
+#[doc = "-   Scaling: `A*alpha`"]
+#[doc = "-   Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A`"]
+#[doc = "-   Matrix multiplication: `A*B`"]
+#[doc = "-   Transposition: `A.t()` (means A<sup>T</sup>)"]
+#[doc = "-   Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems:"]
+#[doc = "`A.inv([method]) (~ A<sup>-1</sup>)`,   `A.inv([method])*B (~ X: AX=B)`"]
+#[doc = "-   Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of"]
+#[doc = "`>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose"]
+#[doc = "elements are set to 255 (if the particular element or pair of elements satisfy the condition) or"]
+#[doc = "0."]
+#[doc = "-   Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of"]
+#[doc = "`&`, `|`, `^`."]
+#[doc = "-   Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)`"]
+#[doc = "-   Element-wise absolute value: `abs(A)`"]
+#[doc = "-   Cross-product, dot-product: `A.cross(B)`, `A.dot(B)`"]
+#[doc = "-   Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm,"]
+#[doc = "mean, sum, countNonZero, trace, determinant, repeat, and others."]
+#[doc = "-   Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated"]
+#[doc = "initializers, matrix constructors and operators that extract sub-matrices (see Mat description)."]
+#[doc = "-   Mat_<destination_type>() constructors to cast the result to the proper type."]
+#[doc = "@note Comma-separated initializers and probably some other operations may require additional"]
+#[doc = "explicit Mat() or Mat_<T>() constructor calls to resolve a possible ambiguity."]
+#[doc = ""]
+#[doc = "Here are examples of matrix expressions:"]
+#[doc = "@code"]
+#[doc = "SVD svd(A);"]
+#[doc = "Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t();"]
+#[doc = ""]
+#[doc = "x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err);"]
+#[doc = ""]
+#[doc = "Mat blurred; double sigma = 1, threshold = 5, amount = 1;"]
+#[doc = "GaussianBlur(img, blurred, Size(), sigma, sigma);"]
+#[doc = "Mat lowContrastMask = abs(img - blurred) < threshold;"]
+#[doc = "Mat sharpened = img*(1+amount) + blurred*(-amount);"]
+#[doc = "img.copyTo(sharpened, lowContrastMask);"]
+#[doc = "@endcode"]
+#[repr(C)]
+#[repr(align(8))]
+pub struct cv_MatExpr {
+    pub _bindgen_opaque_blob: [u64; 44usize],
+}
+#[test]
+fn bindgen_test_layout_cv_MatExpr() {
+    assert_eq!(
+        ::std::mem::size_of::<cv_MatExpr>(),
+        352usize,
+        concat!("Size of: ", stringify!(cv_MatExpr))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<cv_MatExpr>(),
+        8usize,
+        concat!("Alignment of ", stringify!(cv_MatExpr))
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr4sizeEv"]
+    pub fn cv_MatExpr_size(this: *const cv_MatExpr) -> cv_Size;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr4typeEv"]
+    pub fn cv_MatExpr_type(this: *const cv_MatExpr) -> ::std::os::raw::c_int;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr3rowEi"]
+    pub fn cv_MatExpr_row(this: *const cv_MatExpr, y: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr3colEi"]
+    pub fn cv_MatExpr_col(this: *const cv_MatExpr, x: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr4diagEi"]
+    pub fn cv_MatExpr_diag(this: *const cv_MatExpr, d: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr1tEv"]
+    pub fn cv_MatExpr_t(this: *const cv_MatExpr) -> cv_MatExpr;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr3invEi"]
+    pub fn cv_MatExpr_inv(this: *const cv_MatExpr, method: ::std::os::raw::c_int) -> cv_MatExpr;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr3mulERKS0_d"]
+    pub fn cv_MatExpr_mul(this: *const cv_MatExpr, e: *const cv_MatExpr, scale: f64) -> cv_MatExpr;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr3mulERKNS_3MatEd"]
+    pub fn cv_MatExpr_mul1(this: *const cv_MatExpr, m: *const cv_Mat, scale: f64) -> cv_MatExpr;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr5crossERKNS_3MatE"]
+    pub fn cv_MatExpr_cross(this: *const cv_MatExpr, m: *const cv_Mat) -> cv_Mat;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZNK2cv7MatExpr3dotERKNS_3MatE"]
+    pub fn cv_MatExpr_dot(this: *const cv_MatExpr, m: *const cv_Mat) -> f64;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv7MatExpr4swapERS0_"]
+    pub fn cv_MatExpr_swap(this: *mut cv_MatExpr, b: *mut cv_MatExpr);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv7MatExprC1Ev"]
+    pub fn cv_MatExpr_MatExpr(this: *mut cv_MatExpr);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv7MatExprC1ERKNS_3MatE"]
+    pub fn cv_MatExpr_MatExpr1(this: *mut cv_MatExpr, m: *const cv_Mat);
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN2cv7MatExprC1EPKNS_5MatOpEiRKNS_3MatES6_S6_ddRKNS_7Scalar_IdEE"]
+    pub fn cv_MatExpr_MatExpr2(
+        this: *mut cv_MatExpr,
+        _op: *const cv_MatOp,
+        _flags: ::std::os::raw::c_int,
+        _a: *const cv_Mat,
+        _b: *const cv_Mat,
+        _c: *const cv_Mat,
+        _alpha: f64,
+        _beta: f64,
+        _s: *const cv_Scalar,
+    );
+}
+impl cv_MatExpr {
+    #[inline]
+    pub unsafe fn size(&self) -> cv_Size {
+        cv_MatExpr_size(self)
+    }
+    #[inline]
+    pub unsafe fn type_(&self) -> ::std::os::raw::c_int {
+        cv_MatExpr_type(self)
+    }
+    #[inline]
+    pub unsafe fn row(&self, y: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_MatExpr_row(self, y)
+    }
+    #[inline]
+    pub unsafe fn col(&self, x: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_MatExpr_col(self, x)
+    }
+    #[inline]
+    pub unsafe fn diag(&self, d: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_MatExpr_diag(self, d)
+    }
+    #[inline]
+    pub unsafe fn t(&self) -> cv_MatExpr {
+        cv_MatExpr_t(self)
+    }
+    #[inline]
+    pub unsafe fn inv(&self, method: ::std::os::raw::c_int) -> cv_MatExpr {
+        cv_MatExpr_inv(self, method)
+    }
+    #[inline]
+    pub unsafe fn mul(&self, e: *const cv_MatExpr, scale: f64) -> cv_MatExpr {
+        cv_MatExpr_mul(self, e, scale)
+    }
+    #[inline]
+    pub unsafe fn mul1(&self, m: *const cv_Mat, scale: f64) -> cv_MatExpr {
+        cv_MatExpr_mul1(self, m, scale)
+    }
+    #[inline]
+    pub unsafe fn cross(&self, m: *const cv_Mat) -> cv_Mat {
+        cv_MatExpr_cross(self, m)
+    }
+    #[inline]
+    pub unsafe fn dot(&self, m: *const cv_Mat) -> f64 {
+        cv_MatExpr_dot(self, m)
+    }
+    #[inline]
+    pub unsafe fn swap(&mut self, b: *mut cv_MatExpr) {
+        cv_MatExpr_swap(self, b)
+    }
+    #[inline]
+    pub unsafe fn new() -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_MatExpr_MatExpr(__bindgen_tmp.as_mut_ptr());
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new1(m: *const cv_Mat) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_MatExpr_MatExpr1(__bindgen_tmp.as_mut_ptr(), m);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn new2(
+        _op: *const cv_MatOp,
+        _flags: ::std::os::raw::c_int,
+        _a: *const cv_Mat,
+        _b: *const cv_Mat,
+        _c: *const cv_Mat,
+        _alpha: f64,
+        _beta: f64,
+        _s: *const cv_Scalar,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        cv_MatExpr_MatExpr2(
+            __bindgen_tmp.as_mut_ptr(),
+            _op,
+            _flags,
+            _a,
+            _b,
+            _c,
+            _alpha,
+            _beta,
+            _s,
+        );
+        __bindgen_tmp.assume_init()
+    }
+}
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct Landmark {
+    pub x: f32,
+    pub y: f32,
+    pub z: f32,
+    pub visibility: f32,
+    pub presence: f32,
+}
+#[test]
+fn bindgen_test_layout_Landmark() {
+    assert_eq!(
+        ::std::mem::size_of::<Landmark>(),
+        20usize,
+        concat!("Size of: ", stringify!(Landmark))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<Landmark>(),
+        4usize,
+        concat!("Alignment of ", stringify!(Landmark))
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<Landmark>())).x as *const _ as usize },
+        0usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(Landmark),
+            "::",
+            stringify!(x)
+        )
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<Landmark>())).y as *const _ as usize },
+        4usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(Landmark),
+            "::",
+            stringify!(y)
+        )
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<Landmark>())).z as *const _ as usize },
+        8usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(Landmark),
+            "::",
+            stringify!(z)
+        )
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<Landmark>())).visibility as *const _ as usize },
+        12usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(Landmark),
+            "::",
+            stringify!(visibility)
+        )
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<Landmark>())).presence as *const _ as usize },
+        16usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(Landmark),
+            "::",
+            stringify!(presence)
+        )
+    );
+}
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct Pose {
+    pub data: [Landmark; 33usize],
+}
+#[test]
+fn bindgen_test_layout_Pose() {
+    assert_eq!(
+        ::std::mem::size_of::<Pose>(),
+        660usize,
+        concat!("Size of: ", stringify!(Pose))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<Pose>(),
+        4usize,
+        concat!("Alignment of ", stringify!(Pose))
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<Pose>())).data as *const _ as usize },
+        0usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(Pose),
+            "::",
+            stringify!(data)
+        )
+    );
+}
+#[repr(C)]
+#[derive(Debug)]
+pub struct PoseGraph {
+    pub poller: *mut ::std::os::raw::c_void,
+    pub graph: *mut ::std::os::raw::c_void,
+}
+#[test]
+fn bindgen_test_layout_PoseGraph() {
+    assert_eq!(
+        ::std::mem::size_of::<PoseGraph>(),
+        16usize,
+        concat!("Size of: ", stringify!(PoseGraph))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<PoseGraph>(),
+        8usize,
+        concat!("Alignment of ", stringify!(PoseGraph))
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<PoseGraph>())).poller as *const _ as usize },
+        0usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(PoseGraph),
+            "::",
+            stringify!(poller)
+        )
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<PoseGraph>())).graph as *const _ as usize },
+        8usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(PoseGraph),
+            "::",
+            stringify!(graph)
+        )
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN9PoseGraph7processEPKN2cv3MatER4Pose"]
+    pub fn PoseGraph_process(this: *mut PoseGraph, input: *const cv_Mat, output: *mut Pose)
+        -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN9PoseGraphC1EPKcS1_"]
+    pub fn PoseGraph_PoseGraph(
+        this: *mut PoseGraph,
+        graph_config: *const ::std::os::raw::c_char,
+        output_node: *const ::std::os::raw::c_char,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN9PoseGraphD1Ev"]
+    pub fn PoseGraph_PoseGraph_destructor(this: *mut PoseGraph);
+}
+impl PoseGraph {
+    #[inline]
+    pub unsafe fn process(&mut self, input: *const cv_Mat, output: *mut Pose) -> bool {
+        PoseGraph_process(self, input, output)
+    }
+    #[inline]
+    pub unsafe fn new(
+        graph_config: *const ::std::os::raw::c_char,
+        output_node: *const ::std::os::raw::c_char,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        PoseGraph_PoseGraph(__bindgen_tmp.as_mut_ptr(), graph_config, output_node);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn destruct(&mut self) {
+        PoseGraph_PoseGraph_destructor(self)
+    }
+}
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct Hand {
+    pub data: [Landmark; 21usize],
+}
+#[test]
+fn bindgen_test_layout_Hand() {
+    assert_eq!(
+        ::std::mem::size_of::<Hand>(),
+        420usize,
+        concat!("Size of: ", stringify!(Hand))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<Hand>(),
+        4usize,
+        concat!("Alignment of ", stringify!(Hand))
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<Hand>())).data as *const _ as usize },
+        0usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(Hand),
+            "::",
+            stringify!(data)
+        )
+    );
+}
+#[repr(C)]
+#[derive(Debug)]
+pub struct HandsGraph {
+    pub poller: *mut ::std::os::raw::c_void,
+    pub graph: *mut ::std::os::raw::c_void,
+}
+#[test]
+fn bindgen_test_layout_HandsGraph() {
+    assert_eq!(
+        ::std::mem::size_of::<HandsGraph>(),
+        16usize,
+        concat!("Size of: ", stringify!(HandsGraph))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<HandsGraph>(),
+        8usize,
+        concat!("Alignment of ", stringify!(HandsGraph))
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<HandsGraph>())).poller as *const _ as usize },
+        0usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(HandsGraph),
+            "::",
+            stringify!(poller)
+        )
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<HandsGraph>())).graph as *const _ as usize },
+        8usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(HandsGraph),
+            "::",
+            stringify!(graph)
+        )
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN10HandsGraph7processEPKN2cv3MatER4HandS5_"]
+    pub fn HandsGraph_process(
+        this: *mut HandsGraph,
+        input: *const cv_Mat,
+        left: *mut Hand,
+        right: *mut Hand,
+    ) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN10HandsGraphC1EPKcS1_"]
+    pub fn HandsGraph_HandsGraph(
+        this: *mut HandsGraph,
+        graph_config: *const ::std::os::raw::c_char,
+        output_node: *const ::std::os::raw::c_char,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN10HandsGraphD1Ev"]
+    pub fn HandsGraph_HandsGraph_destructor(this: *mut HandsGraph);
+}
+impl HandsGraph {
+    #[inline]
+    pub unsafe fn process(
+        &mut self,
+        input: *const cv_Mat,
+        left: *mut Hand,
+        right: *mut Hand,
+    ) -> bool {
+        HandsGraph_process(self, input, left, right)
+    }
+    #[inline]
+    pub unsafe fn new(
+        graph_config: *const ::std::os::raw::c_char,
+        output_node: *const ::std::os::raw::c_char,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        HandsGraph_HandsGraph(__bindgen_tmp.as_mut_ptr(), graph_config, output_node);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn destruct(&mut self) {
+        HandsGraph_HandsGraph_destructor(self)
+    }
+}
+#[repr(C)]
+#[derive(Debug, Copy, Clone)]
+pub struct FaceMesh {
+    pub data: [Landmark; 478usize],
+}
+#[test]
+fn bindgen_test_layout_FaceMesh() {
+    assert_eq!(
+        ::std::mem::size_of::<FaceMesh>(),
+        9560usize,
+        concat!("Size of: ", stringify!(FaceMesh))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<FaceMesh>(),
+        4usize,
+        concat!("Alignment of ", stringify!(FaceMesh))
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<FaceMesh>())).data as *const _ as usize },
+        0usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(FaceMesh),
+            "::",
+            stringify!(data)
+        )
+    );
+}
+#[repr(C)]
+#[derive(Debug)]
+pub struct FaceMeshGraph {
+    pub poller: *mut ::std::os::raw::c_void,
+    pub graph: *mut ::std::os::raw::c_void,
+}
+#[test]
+fn bindgen_test_layout_FaceMeshGraph() {
+    assert_eq!(
+        ::std::mem::size_of::<FaceMeshGraph>(),
+        16usize,
+        concat!("Size of: ", stringify!(FaceMeshGraph))
+    );
+    assert_eq!(
+        ::std::mem::align_of::<FaceMeshGraph>(),
+        8usize,
+        concat!("Alignment of ", stringify!(FaceMeshGraph))
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<FaceMeshGraph>())).poller as *const _ as usize },
+        0usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(FaceMeshGraph),
+            "::",
+            stringify!(poller)
+        )
+    );
+    assert_eq!(
+        unsafe { &(*(::std::ptr::null::<FaceMeshGraph>())).graph as *const _ as usize },
+        8usize,
+        concat!(
+            "Offset of field: ",
+            stringify!(FaceMeshGraph),
+            "::",
+            stringify!(graph)
+        )
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN13FaceMeshGraph7processEPKN2cv3MatER8FaceMesh"]
+    pub fn FaceMeshGraph_process(
+        this: *mut FaceMeshGraph,
+        input: *const cv_Mat,
+        mesh: *mut FaceMesh,
+    ) -> bool;
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN13FaceMeshGraphC1EPKcS1_"]
+    pub fn FaceMeshGraph_FaceMeshGraph(
+        this: *mut FaceMeshGraph,
+        graph_config: *const ::std::os::raw::c_char,
+        output_node: *const ::std::os::raw::c_char,
+    );
+}
+extern "C" {
+    #[link_name = "\u{1}_ZN13FaceMeshGraphD1Ev"]
+    pub fn FaceMeshGraph_FaceMeshGraph_destructor(this: *mut FaceMeshGraph);
+}
+impl FaceMeshGraph {
+    #[inline]
+    pub unsafe fn process(&mut self, input: *const cv_Mat, mesh: *mut FaceMesh) -> bool {
+        FaceMeshGraph_process(self, input, mesh)
+    }
+    #[inline]
+    pub unsafe fn new(
+        graph_config: *const ::std::os::raw::c_char,
+        output_node: *const ::std::os::raw::c_char,
+    ) -> Self {
+        let mut __bindgen_tmp = ::std::mem::MaybeUninit::uninit();
+        FaceMeshGraph_FaceMeshGraph(__bindgen_tmp.as_mut_ptr(), graph_config, output_node);
+        __bindgen_tmp.assume_init()
+    }
+    #[inline]
+    pub unsafe fn destruct(&mut self) {
+        FaceMeshGraph_FaceMeshGraph_destructor(self)
+    }
+}
diff --git a/src/face_mesh_desktop_live.txt b/src/face_mesh_desktop_live.txt
new file mode 100644
index 0000000..2cc5634
--- /dev/null
+++ b/src/face_mesh_desktop_live.txt
@@ -0,0 +1,66 @@
+# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
+
+# Input image. (ImageFrame)
+input_stream: "input_video"
+
+# Output image with rendered results. (ImageFrame)
+output_stream: "output_video"
+# Collection of detected/processed faces, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "multi_face_landmarks"
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Defines side packets for further use in the graph.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:0:num_faces"
+  output_side_packet: "PACKET:1:with_attention"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 1 }
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Subgraph that detects faces and corresponding landmarks.
+node {
+  calculator: "FaceLandmarkFrontCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_side_packet: "NUM_FACES:num_faces"
+  input_side_packet: "WITH_ATTENTION:with_attention"
+  output_stream: "LANDMARKS:multi_face_landmarks"
+  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
+  output_stream: "DETECTIONS:face_detections"
+  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
+}
+
+# Subgraph that renders face-landmark annotation onto the input image.
+node {
+  calculator: "FaceRendererCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LANDMARKS:multi_face_landmarks"
+  input_stream: "NORM_RECTS:face_rects_from_landmarks"
+  input_stream: "DETECTIONS:face_detections"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/src/hand_tracking_desktop_live.txt b/src/hand_tracking_desktop_live.txt
new file mode 100644
index 0000000..4f34c55
--- /dev/null
+++ b/src/hand_tracking_desktop_live.txt
@@ -0,0 +1,50 @@
+# MediaPipe graph that performs hands tracking on desktop with TensorFlow
+# Lite on CPU.
+# Used in the example in
+# mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu.
+
+# CPU image. (ImageFrame)
+input_stream: "input_video"
+
+# CPU image. (ImageFrame)
+output_stream: "output_video"
+
+# Collection of detected/predicted hands, each represented as a list of
+# landmarks. (std::vector<NormalizedLandmarkList>)
+output_stream: "hand_landmarks"
+
+# Generates side packet cotaining max number of hands to detect/track.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:num_hands"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { int_value: 2 }
+    }
+  }
+}
+
+# Detects/tracks hand landmarks.
+node {
+  calculator: "HandLandmarkTrackingCpu"
+  input_stream: "IMAGE:input_video"
+  input_side_packet: "NUM_HANDS:num_hands"
+  output_stream: "LANDMARKS:hand_landmarks"
+  output_stream: "HANDEDNESS:handedness"
+  output_stream: "PALM_DETECTIONS:multi_palm_detections"
+  output_stream: "HAND_ROIS_FROM_LANDMARKS:multi_hand_rects"
+  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:multi_palm_rects"
+}
+
+# Subgraph that renders annotations and overlays them on top of the input
+# images (see hand_renderer_cpu.pbtxt).
+node {
+  calculator: "HandRendererSubgraph"
+  input_stream: "IMAGE:input_video"
+  input_stream: "DETECTIONS:multi_palm_detections"
+  input_stream: "LANDMARKS:hand_landmarks"
+  input_stream: "HANDEDNESS:handedness"
+  input_stream: "NORM_RECTS:0:multi_palm_rects"
+  input_stream: "NORM_RECTS:1:multi_hand_rects"
+  output_stream: "IMAGE:output_video"
+}
diff --git a/src/lib.rs b/src/lib.rs
new file mode 100644
index 0000000..37c26c2
--- /dev/null
+++ b/src/lib.rs
@@ -0,0 +1,394 @@
+#![allow(dead_code)]
+#![allow(non_camel_case_types)]
+#![allow(non_upper_case_globals)]
+#![allow(non_snake_case)]
+#![allow(improper_ctypes)]
+#![allow(deref_nullptr)]
+
+// -------------------------------------------------------------------------------------------------
+// LINKING
+// -------------------------------------------------------------------------------------------------
+
+#[link(name = "mediagraph")]
+extern "C" {}
+
+use opencv::prelude::*;
+use std::ffi::CString;
+
+mod bindings;
+
+pub use bindings::*;
+
+impl Default for Landmark {
+    fn default() -> Self {
+        Self {
+            x: 0.0,
+            y: 0.0,
+            z: 0.0,
+            visibility: 0.0,
+            presence: 0.0,
+        }
+    }
+}
+
+impl Default for Pose {
+    fn default() -> Self {
+        Self {
+            data: [Landmark::default(); 33],
+        }
+    }
+}
+
+impl Default for Hand {
+    fn default() -> Self {
+        Self {
+            data: [Landmark::default(); 21],
+        }
+    }
+}
+
+impl Default for FaceMesh {
+    fn default() -> Self {
+        Self {
+            data: [Landmark::default(); 478],
+        }
+    }
+}
+
+pub mod pose {
+    use super::*;
+
+    pub enum PoseLandmark {
+        NOSE = 0,
+        LEFT_EYE_INNER = 1,
+        LEFT_EYE = 2,
+        LEFT_EYE_OUTER = 3,
+        RIGHT_EYE_INNER = 4,
+        RIGHT_EYE = 5,
+        RIGHT_EYE_OUTER = 6,
+        LEFT_EAR = 7,
+        RIGHT_EAR = 8,
+        MOUTH_LEFT = 9,
+        MOUTH_RIGHT = 10,
+        LEFT_SHOULDER = 11,
+        RIGHT_SHOULDER = 12,
+        LEFT_ELBOW = 13,
+        RIGHT_ELBOW = 14,
+        LEFT_WRIST = 15,
+        RIGHT_WRIST = 16,
+        LEFT_PINKY = 17,
+        RIGHT_PINKY = 18,
+        LEFT_INDEX = 19,
+        RIGHT_INDEX = 20,
+        LEFT_THUMB = 21,
+        RIGHT_THUMB = 22,
+        LEFT_HIP = 23,
+        RIGHT_HIP = 24,
+        LEFT_KNEE = 25,
+        RIGHT_KNEE = 26,
+        LEFT_ANKLE = 27,
+        RIGHT_ANKLE = 28,
+        LEFT_HEEL = 29,
+        RIGHT_HEEL = 30,
+        LEFT_FOOT_INDEX = 31,
+        RIGHT_FOOT_INDEX = 32,
+    }
+
+    pub struct PoseDetector {
+        pub mode: bool,         // false
+        pub smooth: bool,       // true,
+        pub detection_con: f32, // 0.5
+        pub track_con: f32,     // 0.5
+        pub graph: PoseGraph,
+    }
+
+    impl PoseDetector {
+        pub fn new(mode: bool, smooth: bool, detection_con: f32, track_con: f32) -> Self {
+            let graph_config =
+                CString::new(include_str!("pose_tracking_cpu.txt")).expect("CString::new failed");
+            let output_node = CString::new("pose_landmarks").expect("CString::new failed");
+
+            let graph: PoseGraph =
+                unsafe { PoseGraph::new(graph_config.as_ptr(), output_node.as_ptr()) };
+
+            Self {
+                mode,
+                smooth,
+                detection_con,
+                track_con,
+                graph,
+            }
+        }
+
+        pub fn process(&mut self, input: &Mat, pose: *mut Pose) -> bool {
+            unsafe {
+                let frame = input.as_raw() as *const cv_Mat;
+                self.graph.process(frame, pose)
+            }
+        }
+
+        // // draw true
+        // pub fn find_pose(&self, img: &[u8], draw: bool) {}
+
+        // // draw: true, bbox_with_hands: false
+        // pub fn find_position(&self, img: &[u8], draw: bool, bbox_with_hands: bool) {}
+
+        // // draw: true
+        // pub fn find_angle(
+        //     &self,
+        //     img: &[u8],
+        //     p1: cgmath::Point2<f32>,
+        //     p2: cgmath::Point2<f32>,
+        //     draw: bool,
+        // ) {
+        // }
+
+        // pub fn find_distance(
+        //     &self,
+        //     p1: cgmath::Point2<f32>,
+        //     p2: cgmath::Point2<f32>,
+        //     img: Option<&[u8]>,
+        //     r: f32,
+        //     t: f32,
+        // ) {
+        // }
+
+        // pub fn anlge_check(&self, my_angle: f32, target_angle: f32, add_on: f32) {}
+    }
+
+    impl Default for PoseDetector {
+        fn default() -> Self {
+            Self::new(false, true, 0.5, 0.5)
+        }
+    }
+}
+
+pub mod face_mesh {
+    use super::*;
+
+    pub struct FaceMeshDetector {
+        pub static_mode: bool,      // false
+        pub max_faces: usize,       // 2
+        pub min_detection_con: f32, // 0.5
+        pub min_track_con: f32,     // 0.5
+        pub graph: FaceMeshGraph,
+    }
+
+    impl FaceMeshDetector {
+        pub fn new(
+            static_mode: bool,
+            max_faces: usize,
+            min_detection_con: f32,
+            min_track_con: f32,
+        ) -> Self {
+            let graph_config = CString::new(include_str!("face_mesh_desktop_live.txt"))
+                .expect("CString::new failed");
+            let output_node = CString::new("multi_face_landmarks").expect("CString::new failed");
+
+            let graph: FaceMeshGraph =
+                unsafe { FaceMeshGraph::new(graph_config.as_ptr(), output_node.as_ptr()) };
+            Self {
+                static_mode,
+                max_faces,
+                min_detection_con,
+                min_track_con,
+                graph,
+            }
+        }
+
+        pub fn process(&mut self, input: &Mat, mesh: *mut FaceMesh) -> bool {
+            unsafe {
+                let frame = input.as_raw() as *const cv_Mat;
+                self.graph.process(frame, mesh)
+            }
+        }
+        // // draw: true
+        // pub fn find_face_mesh(&self, img: &[u8], draw: bool) {}
+
+        // pub fn find_distance(
+        //     &self,
+        //     p1: cgmath::Point2<f32>,
+        //     p2: cgmath::Point2<f32>,
+        //     img: Option<&[u8]>,
+        // ) {
+        // }
+    }
+
+    impl Default for FaceMeshDetector {
+        fn default() -> Self {
+            Self::new(false, 2, 0.5, 0.5)
+        }
+    }
+}
+
+// pub mod face_detection {
+//     pub enum FaceKeyPoint {
+//         RIGHT_EYE = 0,
+//         LEFT_EYE = 1,
+//         NOSE_TIP = 2,
+//         MOUTH_CENTER = 3,
+//         RIGHT_EAR_TRAGION = 4,
+//         LEFT_EAR_TRAGION = 5,
+//     }
+//     pub struct FaceDetection {}
+
+//     impl FaceDetection {
+//         pub fn process(&self /* image */) /*NamedTuple*/ {}
+//     }
+
+//     pub struct FaceDetector {
+//         pub min_detection_con: f32, // 0.5
+//         pub face_detection: FaceDetection,
+//     }
+
+//     impl FaceDetector {
+//         pub fn new(min_detection_con: f32) -> Self {
+//             Self {
+//                 min_detection_con,
+//                 face_detection: todo!(),
+//             }
+//         }
+
+//         // draw: true
+//         pub fn find_faces(&self, img: &[u8], draw: bool) {}
+//     }
+
+//     impl Default for FaceDetector {
+//         fn default() -> Self {
+//             Self::new(0.5)
+//         }
+//     }
+// }
+
+pub mod hands {
+    use super::*;
+    // use mediapipe::*;
+    // use std::collections::HashMap;
+
+    pub enum HandLandmark {
+        WRIST = 0,
+        THUMB_CMC = 1,
+        THUMB_MCP = 2,
+        THUMB_IP = 3,
+        THUMB_TIP = 4,
+        INDEX_FINGER_MCP = 5,
+        INDEX_FINGER_PIP = 6,
+        INDEX_FINGER_DIP = 7,
+        INDEX_FINGER_TIP = 8,
+        MIDDLE_FINGER_MCP = 9,
+        MIDDLE_FINGER_PIP = 10,
+        MIDDLE_FINGER_DIP = 11,
+        MIDDLE_FINGER_TIP = 12,
+        RING_FINGER_MCP = 13,
+        RING_FINGER_PIP = 14,
+        RING_FINGER_DIP = 15,
+        RING_FINGER_TIP = 16,
+        PINKY_MCP = 17,
+        PINKY_PIP = 18,
+        PINKY_DIP = 19,
+        PINKY_TIP = 20,
+    }
+
+    pub struct HandDetector {
+        pub mode: bool,
+        pub max_hands: usize,
+        pub detection_con: f32, // 0.5
+        pub min_track_con: f32, // 0.5
+        pub graph: HandsGraph,
+    }
+
+    impl HandDetector {
+        pub fn new(mode: bool, max_hands: usize, detection_con: f32, min_track_con: f32) -> Self {
+            // // ::std::vector<::mediapipe::NormalizedLandmarkList>
+            // let graph_config = CString::new(include_str!("face_mesh_desktop_live.txt")).expect("CString::new failed");
+            // let output_node = CString::new("multi_face_landmarks").expect("CString::new failed");
+
+            let graph_config = CString::new(include_str!("hand_tracking_desktop_live.txt"))
+                .expect("CString::new failed");
+            let output_node = CString::new("hand_landmarks").expect("CString::new failed");
+
+            let graph: HandsGraph =
+                unsafe { HandsGraph::new(graph_config.as_ptr(), output_node.as_ptr()) };
+
+            Self {
+                mode,
+                max_hands,
+                detection_con,
+                min_track_con,
+                graph,
+            }
+        }
+
+        pub fn process(&mut self, input: &Mat, left: *mut Hand, right: *mut Hand) -> bool {
+            unsafe {
+                let frame = input.as_raw() as *const cv_Mat;
+                self.graph.process(frame, left, right)
+            }
+        }
+        // // draw: true, flip_type: tru
+        // pub fn find_hands(&self, img: &[u8], draw: bool, flip_type: bool) {}
+
+        // pub fn fingers_up(&self, my_hand: &HashMap<String, String>) /*List of which fingers are up*/
+        // {
+        // }
+
+        // pub fn find_distance(
+        //     &self,
+        //     p1: cgmath::Point2<f32>,
+        //     p2: cgmath::Point2<f32>,
+        //     img: Option<&[u8]>,
+        // ) {
+        // }
+    }
+
+    impl Default for HandDetector {
+        fn default() -> Self {
+            Self::new(false, 2, 0.5, 0.5)
+        }
+    }
+}
+
+// pub mod objectron {
+//     pub struct Objectron {}
+
+//     impl Objectron {
+//         pub fn process(&self /* image */) /*NamedTuple*/ {}
+//     }
+// }
+
+// pub mod selfie_segmentation {
+//     pub struct SelfieSegmentation {}
+
+//     impl SelfieSegmentation {
+//         pub fn process(&self /* image */) /*NamedTuple*/ {}
+//     }
+
+//     pub struct SelfieSegmentationDetector {
+//         pub model: usize, // 0 is general 1 is landscape(faster)
+//         pub selfie_segmentation: SelfieSegmentation,
+//     }
+
+//     impl SelfieSegmentationDetector {
+//         pub fn new(model: usize) -> Self {
+//             todo!()
+//         }
+
+//         // threshold: 0.1
+//         pub fn remove_bg(&self, img: &[u8], img_bg: [u8; 3], threshold: f32) {}
+//     }
+
+//     impl Default for SelfieSegmentationDetector {
+//         fn default() -> Self {
+//             Self::new(1)
+//         }
+//     }
+// }
+
+#[cfg(test)]
+mod tests {
+    #[test]
+    fn it_works() {
+        let result = 2 + 2;
+        assert_eq!(result, 4);
+    }
+}
diff --git a/src/pose_tracking_cpu.txt b/src/pose_tracking_cpu.txt
new file mode 100644
index 0000000..31d847e
--- /dev/null
+++ b/src/pose_tracking_cpu.txt
@@ -0,0 +1,63 @@
+# MediaPipe graph that performs pose tracking with TensorFlow Lite on CPU.
+
+# CPU buffer. (ImageFrame)
+input_stream: "input_video"
+
+# Output image with rendered results. (ImageFrame)
+output_stream: "output_video"
+# Pose landmarks. (NormalizedLandmarkList)
+output_stream: "pose_landmarks"
+
+# Generates side packet to enable segmentation.
+node {
+  calculator: "ConstantSidePacketCalculator"
+  output_side_packet: "PACKET:enable_segmentation"
+  node_options: {
+    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
+      packet { bool_value: true }
+    }
+  }
+}
+
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for downstream nodes
+# (calculators and subgraphs) in the graph to finish their tasks before it
+# passes through another image. All images that come in while waiting are
+# dropped, limiting the number of in-flight images in most part of the graph to
+# 1. This prevents the downstream nodes from queuing up incoming images and data
+# excessively, which leads to increased latency and memory usage, unwanted in
+# real-time mobile applications. It also eliminates unnecessarily computation,
+# e.g., the output produced by a node may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
+node {
+  calculator: "FlowLimiterCalculator"
+  input_stream: "input_video"
+  input_stream: "FINISHED:output_video"
+  input_stream_info: {
+    tag_index: "FINISHED"
+    back_edge: true
+  }
+  output_stream: "throttled_input_video"
+}
+
+# Subgraph that detects poses and corresponding landmarks.
+node {
+  calculator: "PoseLandmarkCpu"
+  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
+  input_stream: "IMAGE:throttled_input_video"
+  output_stream: "LANDMARKS:pose_landmarks"
+  output_stream: "SEGMENTATION_MASK:segmentation_mask"
+  output_stream: "DETECTION:pose_detection"
+  output_stream: "ROI_FROM_LANDMARKS:roi_from_landmarks"
+}
+
+# Subgraph that renders pose-landmark annotation onto the input image.
+node {
+  calculator: "PoseRendererCpu"
+  input_stream: "IMAGE:throttled_input_video"
+  input_stream: "LANDMARKS:pose_landmarks"
+  input_stream: "SEGMENTATION_MASK:segmentation_mask"
+  input_stream: "DETECTION:pose_detection"
+  input_stream: "ROI:roi_from_landmarks"
+  output_stream: "IMAGE:output_video"
+}