Papers › TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments

TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments

5 Jun 2023CVPR 2023 1arXiv:2306.02850archive 2025-07-28

Yu Sun, Qian Bao, Wu Liu, Tao Mei, Michael J. Black

Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current methods still cannot reliably estimate moving humans in global coordinates, which is critical for many applications. This is particularly challenging when the camera is also moving, entangling human and camera motion. To address these issues, we adopt a novel 5D representation (space, time, and identity) that enables end-to-end reasoning about people in scenes. Our method, called TRACE, introduces several novel architectural components. Most importantly, it uses two new "maps" to reason about the 3D trajectory of people over time in camera, and world, coordinates. An additional memory unit enables persistent tracking of people even during long occlusions. TRACE is the first one-stage method to jointly recover and track 3D humans in global coordinates from dynamic cameras. By training it end-to-end, and using full image information, TRACE achieves state-of-the-art performance on tracking and HPS benchmarks. The code and dataset are released for research purposes.

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cam2pixel Arthur151/ROMP/romp/lib/dataset/MuPoTS.py official repository ran fingerprinted Apache-2.0 (permissive) · 5bbf1999f902db71 · report
conv3x3 Arthur151/ROMP/simple_romp/romp/model.py official repository ran · our draft was wrong Apache-2.0 (permissive) · fac5364e2f53c6db · report
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Tasks

3D Human Pose Estimationregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation 3DPW TRACE MPJPE 79.1 #44 of 119 Archive leaderboard report
3D Human Pose Estimation 3DPW TRACE MPVPE 97.3 #44 of 119 Archive leaderboard report
3D Human Pose Estimation 3DPW TRACE PA-MPJPE 37.8 #44 of 119 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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