cassdlcm/mediapipe
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
bdfdaef305
mediapipe/docs/solutions/face_mesh.md
MediaPipe Team bdfdaef305 Project import generated by Copybara. 
GitOrigin-RevId: b2062656e5b3d33264e28ed0cbca31c4b93fe1bf
2020-07-29 21:18:36 -04:00

8.6 KiB
Raw Blame History

layout title parent nav_order
default Face Mesh Solutions 2

MediaPipe Face Mesh

{: .no_toc }

  1. TOC {:toc}

Overview

MediaPipe Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D surface geometry, requiring only a single camera input without the need for a dedicated depth sensor. Utilizing lightweight model architectures together with GPU acceleration throughout the pipeline, the solution delivers real-time performance critical for live experiences. The core of the solution is the same as what powers YouTube Stories' creator effects, the Augmented Faces API in ARCore and the ML Kit Face Contour Detection API.

face_mesh_ar_effects.gif
Fig 1. AR effects utilizing facial surface geometry.

ML Pipeline

Our ML pipeline consists of two real-time deep neural network models that work together: A detector that operates on the full image and computes face locations and a 3D face landmark model that operates on those locations and predicts the approximate surface geometry via regression. Having the face accurately cropped drastically reduces the need for common data augmentations like affine transformations consisting of rotations, translation and scale changes. Instead it allows the network to dedicate most of its capacity towards coordinate prediction accuracy. In addition, in our pipeline the crops can also be generated based on the face landmarks identified in the previous frame, and only when the landmark model could no longer identify face presence is the face detector invoked to relocalize the face. This strategy is similar to that employed in our MediaPipe Hands solution, which uses a palm detector together with a hand landmark model.

The pipeline is implemented as a MediaPipe graph that uses a face landmark subgraph from the face landmark module, and renders using a dedicated face renderer subgraph. The face landmark subgraph internally uses a face_detection_subgraph from the face detection module.

Note: To visualize a graph, copy the graph and paste it into MediaPipe Visualizer. For more information on how to visualize its associated subgraphs, please see visualizer documentation.

Models

Face Detection Model

The face detector is the same BlazeFace model used in MediaPipe Face Detection. Please refer to MediaPipe Face Detection for details.

Face Landmark Model

For 3D face landmarks we employed transfer learning and trained a network with several objectives: the network simultaneously predicts 3D landmark coordinates on synthetic rendered data and 2D semantic contours on annotated real-world data. The resulting network provided us with reasonable 3D landmark predictions not just on synthetic but also on real-world data.

The 3D landmark network receives as input a cropped video frame without additional depth input. The model outputs the positions of the 3D points, as well as the probability of a face being present and reasonably aligned in the input. A common alternative approach is to predict a 2D heatmap for each landmark, but it is not amenable to depth prediction and has high computational costs for so many points. We further improve the accuracy and robustness of our model by iteratively bootstrapping and refining predictions. That way we can grow our dataset to increasingly challenging cases, such as grimaces, oblique angle and occlusions.

You can find more information about the face landmark model in this paper.

face_mesh_android_gpu.gif
Fig 2. Output of MediaPipe Face Mesh: the red box indicates the cropped area as input to the landmark model, the red dots represent the 468 landmarks in 3D, and the green lines connecting landmarks illustrate the contours around the eyes, eyebrows, lips and the entire face.

Example Apps

Please first see general instructions for Android, iOS and desktop on how to build MediaPipe examples.

Note: To visualize a graph, copy the graph and paste it into MediaPipe Visualizer. For more information on how to visualize its associated subgraphs, please see visualizer documentation.

Mobile

Tip: Maximum number of faces to detect/process is set to 1 by default. To change it, for Android modify NUM_FACES in MainActivity.java, and for iOS modify kNumFaces in ViewController.mm.

Desktop

Tip: Maximum number of faces to detect/process is set to 1 by default. To change it, in the graph file modify the option of ConstantSidePacketCalculator.

Resources