BlazePose: On-device Real-time Body Pose tracking

We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices. During inference, the network produces 33 body keypoints for a single person and runs at over 30 frames per second on a Pixel 2 phone. This makes it particularly suited to real-time use cases like fitness tracking and sign language recognition. Our main contributions include a novel body pose tracking solution and a lightweight body pose estimation neural network that uses both heatmaps and regression to keypoint coordinates.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Pose Estimation Google-AR BlazePose Lite PCK@0.2 79.6 # 3
3D Pose Estimation Google-AR BlazePose Full PCK@0.2 84.1 # 2
3D Pose Estimation Google-AR OpenPose (body only) PCK@0.2 87.8 # 1
3D Pose Estimation Google-Yoga BlazePose Full PCK@0.2 84.5 # 1
3D Pose Estimation Google-Yoga BlazePose Lite PCK@0.2 77.6 # 3
3D Pose Estimation Google-Yoga OpenPose (body only) PCK@0.2 83.4 # 2


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