Papers › Graph Stacked Hourglass Networks for 3D Human Pose Estimation
Graph Stacked Hourglass Networks for 3D Human Pose Estimation
Tianhan Xu, Wataru Takano
In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the performance improvements that result from exploiting multi-scale, multi-level feature representations. Extensive experiments are conducted to validate our approach, and the results show that our model outperforms the state-of-the-art.
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Code
Syntology Ran 12 of 15 code samples harvested from 1 repository linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · violated contract; 1 ran · our draft was wrong; 10 ran with no contract checked.
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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 | Graph Stacked Hourglass Network (CPN) | Average MPJPE (mm) | 51.9 | #73 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Graph Stacked Hourglass Network (CPN) | Multi-View or Monocular | Monocular | #73 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Graph Stacked Hourglass Network (CPN) | Using 2D ground-truth joints | No | #73 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | Graph Stacked Hourglass Network | AUC | 45.8 | #92 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | Graph Stacked Hourglass Network | PCK | 80.1 | #92 of 108 | 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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