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## Publications
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## Publications
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* [BlazePose - On-device Real-time Body Pose Tracking](https://mediapipe.page.link/blazepose-blog)
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* [BlazePose - On-device Real-time Body Pose Tracking](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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in Google AI Blog
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in Google AI Blog
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* [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation](https://ai.googleblog.com/2020/08/mediapipe-iris-real-time-iris-tracking.html)
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* [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation](https://ai.googleblog.com/2020/08/mediapipe-iris-real-time-iris-tracking.html)
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in Google AI Blog
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in Google AI Blog
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## Publications
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## Publications
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* [BlazePose - On-device Real-time Body Pose Tracking](https://mediapipe.page.link/blazepose-blog)
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* [BlazePose - On-device Real-time Body Pose Tracking](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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in Google AI Blog
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in Google AI Blog
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* [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation](https://ai.googleblog.com/2020/08/mediapipe-iris-real-time-iris-tracking.html)
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* [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation](https://ai.googleblog.com/2020/08/mediapipe-iris-real-time-iris-tracking.html)
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in Google AI Blog
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in Google AI Blog
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@ -5,7 +5,7 @@ parent: Solutions
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nav_order: 5
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nav_order: 5
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---
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---
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# MediaPipe Pose
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# MediaPipe BlazePose
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{: .no_toc }
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{: .no_toc }
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1. TOC
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1. TOC
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@ -22,12 +22,13 @@ on top of the physical world in augmented reality.
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MediaPipe Pose is a ML solution for high-fidelity upper-body pose tracking,
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MediaPipe Pose is a ML solution for high-fidelity upper-body pose tracking,
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inferring 25 2D upper-body landmarks from RGB video frames utilizing our
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inferring 25 2D upper-body landmarks from RGB video frames utilizing our
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[BlazePose](https://mediapipe.page.link/blazepose-blog) research. Current
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[BlazePose](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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state-of-the-art approaches rely primarily on powerful desktop environments for
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research. Current state-of-the-art approaches rely primarily on powerful desktop
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inference, whereas our method achieves real-time performance on most modern
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environments for inference, whereas our method achieves real-time performance on
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[mobile phones](#mobile), [desktops/laptops](#desktop), in [python](#python) and
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most modern [mobile phones](#mobile), [desktops/laptops](#desktop), in
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even on the [web](#web). A variant of MediaPipe Pose that performs full-body
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[python](#python) and even on the [web](#web). A variant of MediaPipe Pose that
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pose tracking on mobile phones will be included in an upcoming release of
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performs full-body pose tracking on mobile phones will be included in an
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upcoming release of
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[ML Kit](https://developers.google.com/ml-kit/early-access/pose-detection).
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[ML Kit](https://developers.google.com/ml-kit/early-access/pose-detection).
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![pose_tracking_upper_body_example.gif](../images/mobile/pose_tracking_upper_body_example.gif) |
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![pose_tracking_upper_body_example.gif](../images/mobile/pose_tracking_upper_body_example.gif) |
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(x, y location and visibility), plus two virtual alignment keypoints. It shares
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(x, y location and visibility), plus two virtual alignment keypoints. It shares
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the same architecture as the full-body version that predicts 33 landmarks,
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the same architecture as the full-body version that predicts 33 landmarks,
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described in more detail in the
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described in more detail in the
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[BlazePose Google AI Blog](https://mediapipe.page.link/blazepose-blog) and in
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[BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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this [paper](https://arxiv.org/abs/2006.10204).
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and in this [paper](https://arxiv.org/abs/2006.10204).
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![pose_tracking_upper_body_landmarks.png](../images/mobile/pose_tracking_upper_body_landmarks.png) |
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![pose_tracking_upper_body_landmarks.png](../images/mobile/pose_tracking_upper_body_landmarks.png) |
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:------------------------------------------------------------------------------------------------: |
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:------------------------------------------------------------------------------------------------: |
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@ -168,7 +169,7 @@ Please refer to [these instructions](../index.md#mediapipe-on-the-web).
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## Resources
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## Resources
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* Google AI Blog:
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* Google AI Blog:
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[BlazePose - On-device Real-time Body Pose Tracking](https://mediapipe.page.link/blazepose-blog)
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[BlazePose - On-device Real-time Body Pose Tracking](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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* Paper:
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* Paper:
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[BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
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[BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
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([presentation](https://youtu.be/YPpUOTRn5tA))
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([presentation](https://youtu.be/YPpUOTRn5tA))
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* For back-facing cameras: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_back.tflite)
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* For back-facing cameras: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_back.tflite)
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* [Model page](https://sites.google.com/corp/view/perception-cv4arvr/blazeface)
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* [Model page](https://sites.google.com/corp/view/perception-cv4arvr/blazeface)
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* Paper: ["BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs"](https://arxiv.org/abs/1907.05047)
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* Paper: ["BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs"](https://arxiv.org/abs/1907.05047)
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* [Model card](https://sites.google.com/corp/view/perception-cv4arvr/blazeface#h.p_21ojPZDx3cqq)
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* [Model card](https://mediapipe.page.link/blazeface-mc)
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### Face Mesh
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### Face Mesh
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* Face detection: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite) (see above)
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* Face detection: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite) (see above)
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* Paper: ["Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs"](https://arxiv.org/abs/1907.06724)
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* Paper: ["Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs"](https://arxiv.org/abs/1907.06724)
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* [Google AI Blog post](https://ai.googleblog.com/2019/03/real-time-ar-self-expression-with.html)
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* [Google AI Blog post](https://ai.googleblog.com/2019/03/real-time-ar-self-expression-with.html)
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* [TensorFlow Blog post](https://blog.tensorflow.org/2020/03/face-and-hand-tracking-in-browser-with-mediapipe-and-tensorflowjs.html)
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* [TensorFlow Blog post](https://blog.tensorflow.org/2020/03/face-and-hand-tracking-in-browser-with-mediapipe-and-tensorflowjs.html)
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* [Model card](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)
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* [Model card](https://mediapipe.page.link/facemesh-mc)
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### Hand Detection and Tracking
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### Hand Detection and Tracking
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* Palm detection: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/palm_detection.tflite), [TF.js model](https://tfhub.dev/mediapipe/handdetector/1)
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* Palm detection: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/palm_detection.tflite), [TF.js model](https://tfhub.dev/mediapipe/handdetector/1)
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* 3D hand landmarks: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/hand_landmark.tflite), [TF.js model](https://tfhub.dev/mediapipe/handskeleton/1)
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* 3D hand landmarks: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/hand_landmark.tflite), [TF.js model](https://tfhub.dev/mediapipe/handskeleton/1)
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* [Google AI Blog post](https://mediapipe.page.link/handgoogleaiblog)
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* [Google AI Blog post](https://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html)
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* [TensorFlow Blog post](https://blog.tensorflow.org/2020/03/face-and-hand-tracking-in-browser-with-mediapipe-and-tensorflowjs.html)
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* [TensorFlow Blog post](https://blog.tensorflow.org/2020/03/face-and-hand-tracking-in-browser-with-mediapipe-and-tensorflowjs.html)
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* [Model card](https://mediapipe.page.link/handmc)
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* [Model card](https://mediapipe.page.link/handmc)
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[BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
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[BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
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([presentation](https://youtu.be/YPpUOTRn5tA))
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([presentation](https://youtu.be/YPpUOTRn5tA))
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* Google AI Blog:
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* Google AI Blog:
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[BlazePose - On-device Real-time Body Pose Tracking](https://mediapipe.page.link/blazepose-blog)
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[BlazePose - On-device Real-time Body Pose Tracking](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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* [Model card](https://mediapipe.page.link/blazepose-mc)
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* [Model card](https://mediapipe.page.link/blazepose-mc)
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### Hair Segmentation
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### Hair Segmentation
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* [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/hair_segmentation.tflite)
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* [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/hair_segmentation.tflite)
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* [Model page](https://sites.google.com/corp/view/perception-cv4arvr/hair-segmentation)
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* [Model page](https://sites.google.com/corp/view/perception-cv4arvr/hair-segmentation)
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* Paper: ["Real-time Hair segmentation and recoloring on Mobile GPUs"](https://arxiv.org/abs/1907.06740)
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* Paper: ["Real-time Hair segmentation and recoloring on Mobile GPUs"](https://arxiv.org/abs/1907.06740)
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* [Model card](https://drive.google.com/file/d/1lPwJ8BD_-3UUor4LayQ0xpa_RIC_hoRh/view)
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* [Model card](https://mediapipe.page.link/hairsegmentation-mc)
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### Objectron (3D Object Detection)
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### Objectron (3D Object Detection)
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* Shoes: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_sneakers.tflite)
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* Shoes: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_sneakers.tflite)
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* Up to 200 keypoints: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float.tflite)
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* Up to 200 keypoints: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float.tflite)
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* Up to 400 keypoints: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float_400.tflite)
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* Up to 400 keypoints: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float_400.tflite)
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* Up to 1000 keypoints: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float_1k.tflite)
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* Up to 1000 keypoints: [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/knift_float_1k.tflite)
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* [Google Developers Blog post](https://mediapipe.page.link/knift)
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* [Google Developers Blog post](https://developers.googleblog.com/2020/04/mediapipe-knift-template-based-feature-matching.html)
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* [Model card](https://mediapipe.page.link/knift-mc)
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* [Model card](https://mediapipe.page.link/knift-mc)
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