DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916878929100800 |
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| author | Lu, Junzhe Lin, Jing Dou, Hongkun Zeng, Ailing Deng, Yue Liu, Xian Cai, Zhongang Yang, Lei Zhang, Yulun Wang, Haoqian Liu, Ziwei |
| author_facet | Lu, Junzhe Lin, Jing Dou, Hongkun Zeng, Ailing Deng, Yue Liu, Xian Cai, Zhongang Yang, Lei Zhang, Yulun Wang, Haoqian Liu, Ziwei |
| contents | We present DPoser-X, a diffusion-based prior model for 3D whole-body human poses. Building a versatile and robust full-body human pose prior remains challenging due to the inherent complexity of articulated human poses and the scarcity of high-quality whole-body pose datasets. To address these limitations, we introduce a Diffusion model as body Pose prior (DPoser) and extend it to DPoser-X for expressive whole-body human pose modeling. Our approach unifies various pose-centric tasks as inverse problems, solving them through variational diffusion sampling. To enhance performance on downstream applications, we introduce a novel truncated timestep scheduling method specifically designed for pose data characteristics. We also propose a masked training mechanism that effectively combines whole-body and part-specific datasets, enabling our model to capture interdependencies between body parts while avoiding overfitting to specific actions. Extensive experiments demonstrate DPoser-X's robustness and versatility across multiple benchmarks for body, hand, face, and full-body pose modeling. Our model consistently outperforms state-of-the-art alternatives, establishing a new benchmark for whole-body human pose prior modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00599 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior Lu, Junzhe Lin, Jing Dou, Hongkun Zeng, Ailing Deng, Yue Liu, Xian Cai, Zhongang Yang, Lei Zhang, Yulun Wang, Haoqian Liu, Ziwei Computer Vision and Pattern Recognition We present DPoser-X, a diffusion-based prior model for 3D whole-body human poses. Building a versatile and robust full-body human pose prior remains challenging due to the inherent complexity of articulated human poses and the scarcity of high-quality whole-body pose datasets. To address these limitations, we introduce a Diffusion model as body Pose prior (DPoser) and extend it to DPoser-X for expressive whole-body human pose modeling. Our approach unifies various pose-centric tasks as inverse problems, solving them through variational diffusion sampling. To enhance performance on downstream applications, we introduce a novel truncated timestep scheduling method specifically designed for pose data characteristics. We also propose a masked training mechanism that effectively combines whole-body and part-specific datasets, enabling our model to capture interdependencies between body parts while avoiding overfitting to specific actions. Extensive experiments demonstrate DPoser-X's robustness and versatility across multiple benchmarks for body, hand, face, and full-body pose modeling. Our model consistently outperforms state-of-the-art alternatives, establishing a new benchmark for whole-body human pose prior modeling. |
| title | DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.00599 |