DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior
This addresses the problem of versatile whole-body pose modeling for computer vision applications, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of building a robust 3D whole-body human pose prior by introducing DPoser-X, a diffusion-based model that unifies pose-centric tasks as inverse problems solved through variational diffusion sampling, and it consistently outperforms state-of-the-art alternatives across multiple benchmarks.
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.