Papers › Semantic Graph Convolutional Networks for 3D Human Pose Regression
Semantic Graph Convolutional Networks for 3D Human Pose Regression
Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas
In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.
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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 | Human3.6M | SemGCN | Average MPJPE (mm) | 57.6 | #79 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | SemGCN | Multi-View or Monocular | Monocular | #79 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | SemGCN | Using 2D ground-truth joints | No | #79 of 88 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | SemGCN | Average MPJPE (mm) | 57.6 | #28 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | SemGCN | Frames Needed | 1 | #28 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | SemGCN | Need Ground Truth 2D Pose | No | #28 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | SemGCN | Use Video Sequence | No | #28 of 52 | 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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