DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior
Authors: name: Junzhe Lu, name: Jing Lin, name: Hongkun Dou, name: Ailing Zeng, name: Yue Deng, name: Xian Liu, name: Zhongang Cai, name: Lei Yang, name: Yulun Zhang, name: Haoqian Wang, name: Ziwei Liu

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.