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.
@article{arxiv.2508.00599,
title = {DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior},
author = {Junzhe Lu and Jing Lin and Hongkun Dou and Ailing Zeng and Yue Deng and Xian Liu and Zhongang Cai and Lei Yang and Yulun Zhang and Haoqian Wang and Ziwei Liu},
journal= {arXiv preprint arXiv:2508.00599},
year = {2025}
}