Abstract
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand, diffusion models have recently emerged as an effective tool for generating high-quality images from noise. In-spired by their capability, we explore a novel pose estimation framework (DiffPose) that formulates 3D pose estimation as a reverse diffusion process. We incorporate novel designs into our DiffPose to facilitate the diffusion process for 3D pose estimation: a pose-specific initialization of pose uncertainty distributions, a Gaussian Mixture Model-based forward diffusion process, and a context-conditioned re-verse diffusion process. Our proposed DiffPose significantly outperforms existing methods on the widely used pose estimation benchmarks Human3.6M and MPI-INF-3DHP. Project page: https://gongjia0208.github.io/Diffpose/.
Original language | English |
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Title of host publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023 |
Editors | Eric Mortensen |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 13041-13051 |
Number of pages | 11 |
ISBN (Electronic) | 9798350301298 |
DOIs | |
Publication status | Published - 2023 |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2023 - Vancouver, Canada Duration: 18 Jun 2023 → 22 Jun 2023 https://cvpr2023.thecvf.com/ (Website) https://openaccess.thecvf.com/CVPR2023?day=all (Proceedings) https://ieeexplore.ieee.org/xpl/conhome/10203037/proceeding (Proceedings) https://cvpr2023.thecvf.com/Conferences/2023 (Website) |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2023 |
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Abbreviated title | CVPR 2023 |
Country/Territory | Canada |
City | Vancouver |
Period | 18/06/23 → 22/06/23 |
Internet address |
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Keywords
- 3D from single images