Papers › Learning 3D Human Pose from Structure and Motion
Learning 3D Human Pose from Structure and Motion
Rishabh Dabral, Anurag Mundhada, Uday Kusupati, Safeer Afaque, Abhishek Sharma, Arjun Jain
3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.
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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 | 3DPW | TP-Net | PA-MPJPE | 92.2 | #111 of 119 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | TP-Net | Average MPJPE (mm) | 52.1 | #26 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | TP-Net | Frames Needed | 20 | #26 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | TP-Net | Need Ground Truth 2D Pose | No | #26 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | TP-Net | Use Video Sequence | Yes | #26 of 52 | 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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