  This work targets to construct a robust human pose prior. However, it remains
a persistent challenge due to biomechanical constraints and diverse human
movements. Traditional priors like VAEs and NDFs often exhibit shortcomings in
realism and generalization, notably with unseen noisy poses. To address these
issues, we introduce DPoser, a robust and versatile human pose prior built upon
diffusion models. DPoser regards various pose-centric tasks as inverse problems
and employs variational diffusion sampling for efficient solving. Accordingly,
designed with optimization frameworks, DPoser seamlessly benefits human mesh
recovery, pose generation, pose completion, and motion denoising tasks.
Furthermore, due to the disparity between the articulated poses and structured
images, we propose truncated timestep scheduling to enhance the effectiveness
of DPoser. Our approach demonstrates considerable enhancements over common
uniform scheduling used in image domains, boasting improvements of 5.4%, 17.2%,
and 3.8% across human mesh recovery, pose completion, and motion denoising,
respectively. Comprehensive experiments demonstrate the superiority of DPoser
over existing state-of-the-art pose priors across multiple tasks.
