Papers › MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
Ben Usman, Andrea Tagliasacchi, Kate Saenko, Avneesh Sud
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date. We show how to train a neural model to perform this task with high precision and minimal latency overhead. The proposed model takes into account joint location uncertainty due to occlusion from multiple views, and requires only 2D keypoint data for training. Our method outperforms both classical bundle adjustment and weakly-supervised monocular 3D baselines on the well-established Human3.6M dataset, as well as the more challenging in-the-wild Ski-Pose PTZ dataset.
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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 Human Pose Estimation | SkiPose | MetaPose (S1+S2) | MPJPE | 53 | #1 of 3 | Archive leaderboard | report |
| 3D Human Pose Estimation | SkiPose | MetaPose (S1+S2) | P-MPJPE | 42 | #1 of 3 | Archive leaderboard | report |
| 3D Human Pose Estimation | SkiPose | MetaPose (S1+IR) | MPJPE | 54 | #2 of 3 | Archive leaderboard | report |
| 3D Human Pose Estimation | SkiPose | MetaPose (S1+IR) | P-MPJPE | 30 | #2 of 3 | Archive leaderboard | report |
| Weakly-supervised 3D Human Pose Estimation | Human3.6M | MetaPose (S1+S2/SS) | Average MPJPE (mm) | 56 | #6 of 33 | 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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