Papers › Motion Guided 3D Pose Estimation from Videos
Motion Guided 3D Pose Estimation from Videos
Jingbo Wang, Sijie Yan, Yuanjun Xiong, Dahua Lin
We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced. We design a new graph convolutional network architecture, U-shaped GCN (UGCN). It captures both short-term and long-term motion information to fully leverage the additional supervision from the motion loss. We experiment training UGCN with the motion loss on two large scale benchmarks: Human3.6M and MPI-INF-3DHP. Our model surpasses other state-of-the-art models by a large margin. It also demonstrates strong capacity in producing smooth 3D sequences and recovering keypoint motion.
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Code
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Code Syntology ran Syntology
9 samples harvested; 2 ran; 1 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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 | Human3.6M | UGCN (HR-Net) | Average MPJPE (mm) | 42.6 | #25 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | UGCN (HR-Net) | Multi-View or Monocular | Monocular | #25 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | UGCN (HR-Net) | Using 2D ground-truth joints | No | #25 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | UGCN | AUC | 62.1 | #28 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | UGCN | MPJPE | 68.1 | #28 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | UGCN | PCK | 86.9 | #28 of 108 | 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.
Methods
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