Papers › SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
Jianan Zhen, Qi Fang, Jiaming Sun, Wentao Liu, Wei Jiang, Hujun Bao, Xiaowei Zhou
Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view. Addressing this ambiguity requires to aggregate various cues over the entire image, such as body sizes, scene layouts, and inter-person relationships. However, most previous methods adopt a top-down scheme that first performs 2D pose detection and then regresses the 3D pose and scale for each detected person individually, ignoring global contextual cues. In this paper, we propose a novel system that first regresses a set of 2.5D representations of body parts and then reconstructs the 3D absolute poses based on these 2.5D representations with a depth-aware part association algorithm. Such a single-shot bottom-up scheme allows the system to better learn and reason about the inter-person depth relationship, improving both 3D and 2D pose estimation. The experiments demonstrate that the proposed approach achieves the state-of-the-art performance on the CMU Panoptic and MuPoTS-3D datasets and is applicable to in-the-wild videos.
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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 Multi-Person Pose Estimation | Panoptic | SMAP | Average MPJPE (mm) | 61.8 | #18 of 20 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation (absolute) | MuPoTS-3D | SMAP | 3DPCK | 35.4 | #11 of 14 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation (root-relative) | MuPoTS-3D | SMAP | 3DPCK | 73.5 | #16 of 20 | 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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