Papers › 3D Human Pose Estimation with Spatial and Temporal Transformers
3D Human Pose Estimation with Spatial and Temporal Transformers
Ce Zheng, Sijie Zhu, Matias Mendieta, Taojiannan Yang, Chen Chen, Zhengming Ding
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation. However, in the field of human pose estimation, convolutional architectures still remain dominant. In this work, we present PoseFormer, a purely transformer-based approach for 3D human pose estimation in videos without convolutional architectures involved. Inspired by recent developments in vision transformers, we design a spatial-temporal transformer structure to comprehensively model the human joint relations within each frame as well as the temporal correlations across frames, then output an accurate 3D human pose of the center frame. We quantitatively and qualitatively evaluate our method on two popular and standard benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments show that PoseFormer achieves state-of-the-art performance on both datasets. Code is available at \url{https://github.com/zczcwh/PoseFormer}
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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 | PoseFormer (f=81) | Average MPJPE (mm) | 44.3 | #35 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | PoseFormer (f=81) | Multi-View or Monocular | Monocular | #35 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | PoseFormer (f=81) | Using 2D ground-truth joints | No | #35 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | HumanEva-I | PoseFormer | Mean Reconstruction Error (mm) | 21.6 | #13 of 31 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | PoseFormer (9 frames) | AUC | 56.4 | #36 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | PoseFormer (9 frames) | MPJPE | 77.1 | #36 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | PoseFormer (9 frames) | PCK | 88.6 | #36 of 108 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | PoseFormer (T=81) | 2D detector | CPN | #15 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | PoseFormer (T=81) | Average MPJPE (mm) | 44.3 | #15 of 52 | Archive leaderboard | report |
| Monocular 3D Human Pose Estimation | Human3.6M | PoseFormer (T=81) | Frames Needed | 81 | #15 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.
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