Papers › BlazePose: On-device Real-time Body Pose tracking
BlazePose: On-device Real-time Body Pose tracking
Valentin Bazarevsky, Ivan Grishchenko, Karthik Raveendran, Tyler Zhu, Fan Zhang, Matthias Grundmann
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Pose Estimation | Google-AR | OpenPose (body only) | PCK@0.2 | 87.8 | #1 of 3 | Archive leaderboard | report |
| 3D Pose Estimation | Google-AR | BlazePose Full | PCK@0.2 | 84.1 | #2 of 3 | Archive leaderboard | report |
| 3D Pose Estimation | Google-AR | BlazePose Lite | PCK@0.2 | 79.6 | #3 of 3 | Archive leaderboard | report |
| 3D Pose Estimation | Google-Yoga | BlazePose Full | PCK@0.2 | 84.5 | #1 of 3 | Archive leaderboard | report |
| 3D Pose Estimation | Google-Yoga | OpenPose (body only) | PCK@0.2 | 83.4 | #2 of 3 | Archive leaderboard | report |
| 3D Pose Estimation | Google-Yoga | BlazePose Lite | PCK@0.2 | 77.6 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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