DPoser: Diffusion Model as Robust 3D Human Pose Prior
CoRR(2023)
摘要
Modeling human pose is a cornerstone in applications from human-robot
interaction to augmented reality, yet crafting a robust human pose prior
remains a challenge due to biomechanical constraints and diverse human
movements. Traditional priors like VAEs and NDFs often fall short in realism
and generalization, especially in extreme conditions such as unseen noisy
poses. To address these issues, we introduce DPoser, a robust and versatile
human pose prior built upon diffusion models. Designed with optimization
frameworks, DPoser seamlessly integrates into various pose-centric
applications, including human mesh recovery, pose completion, and motion
denoising. Specifically, by formulating these tasks as inverse problems, we
employ variational diffusion sampling for efficient solving. Furthermore,
acknowledging the disparity between the articulated poses we focus on and
structured images in previous research, we propose a truncated timestep
scheduling to boost performance on downstream tasks. Our exhaustive experiments
demonstrate DPoser's superiority over existing state-of-the-art pose priors
across multiple tasks.
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