Papers › TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
Yufu Wang, ZiYun Wang, Lingjie Liu, Kostas Daniilidis
We propose TRAM, a two-stage method to reconstruct a human's global trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the presence of dynamic humans and uses the scene background to derive the motion scale. Using the recovered camera as a metric-scale reference frame, we introduce a video transformer model (VIMO) to regress the kinematic body motion of a human. By composing the two motions, we achieve accurate recovery of 3D humans in the world space, reducing global motion errors by a large margin from prior work. https://yufu-wang.github.io/tram4d/
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Code
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Code Syntology ran Syntology
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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 | EMDB | TRAM | Average MPJPE (mm) | 74.4 | #1 of 13 | Archive leaderboard | report |
| 3D Human Pose Estimation | EMDB | TRAM | Average MPJPE-PA (mm) | 45.7 | #1 of 13 | Archive leaderboard | report |
| 3D Human Pose Estimation | EMDB | TRAM | Average MVE (mm) | 86.6 | #1 of 13 | 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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