Papers › Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in...
Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
Yu Cheng, Bo wang, Bo Yang, Robby T. Tan
Despite the recent progress, 3D multi-person pose estimation from monocular videos is still challenging due to the commonly encountered problem of missing information caused by occlusion, partially out-of-frame target persons, and inaccurate person detection. To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that do not require camera parameters. In particular, we introduce a human-joint GCN, which, unlike the existing GCN, is based on a directed graph that employs the 2D pose estimator's confidence scores to improve the pose estimation results. We also introduce a human-bone GCN, which models the bone connections and provides more information beyond human joints. The two GCNs work together to estimate the spatial frame-wise 3D poses and can make use of both visible joint and bone information in the target frame to estimate the occluded or missing human-part information. To further refine the 3D pose estimation, we use our temporal convolutional networks (TCNs) to enforce the temporal and human-dynamics constraints. We use a joint-TCN to estimate person-centric 3D poses across frames, and propose a velocity-TCN to estimate the speed of 3D joints to ensure the consistency of the 3D pose estimation in consecutive frames. Finally, to estimate the 3D human poses for multiple persons, we propose a root-TCN that estimates camera-centric 3D poses without requiring camera parameters. Quantitative and qualitative evaluations demonstrate the effectiveness of the proposed method.
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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 Absolute Human Pose Estimation | Human3.6M | GnTCN | MRPE | 88.1 | #1 of 4 | Archive leaderboard | report |
| 3D Human Pose Estimation | 3DPW | GnTCN | PA-MPJPE | 64.2 | #98 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | GnTCN | Average MPJPE (mm) | 40.9 | #20 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | GnTCN | Multi-View or Monocular | Monocular | #20 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | GnTCN | PA-MPJPE | 30.4 | #20 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | GnTCN | Using 2D ground-truth joints | No | #20 of 88 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation (absolute) | MuPoTS-3D | GnTCN | 3DPCK | 45.7 | #3 of 14 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation (root-relative) | MuPoTS-3D | GnTCN | 3DPCK | 87.5 | #3 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.
Methods
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