Papers › Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks
Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks
Yu Cheng, Bo wang, Bo Yang, Robby T. Tan
In monocular video 3D multi-person pose estimation, inter-person occlusion and close interactions can cause human detection to be erroneous and human-joints grouping to be unreliable. Existing top-down methods rely on human detection and thus suffer from these problems. Existing bottom-up methods do not use human detection, but they process all persons at once at the same scale, causing them to be sensitive to multiple-persons scale variations. To address these challenges, we propose the integration of top-down and bottom-up approaches to exploit their strengths. Our top-down network estimates human joints from all persons instead of one in an image patch, making it robust to possible erroneous bounding boxes. Our bottom-up network incorporates human-detection based normalized heatmaps, allowing the network to be more robust in handling scale variations. Finally, the estimated 3D poses from the top-down and bottom-up networks are fed into our integration network for final 3D poses. Besides the integration of top-down and bottom-up networks, unlike existing pose discriminators that are designed solely for single person, and consequently cannot assess natural inter-person interactions, we propose a two-person pose discriminator that enforces natural two-person interactions. Lastly, we also apply a semi-supervised method to overcome the 3D ground-truth data scarcity. Our quantitative and qualitative evaluations show the effectiveness of our method compared to the state-of-the-art baselines.
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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 (absolute) | MuPoTS-3D | TDBU_Net | 3DPCK | 48.0 | #2 of 14 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation (root-relative) | MuPoTS-3D | TDBU_Net | 3DPCK | 89.6 | #1 of 20 | Archive leaderboard | report |
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