Multi-modal Pose Diffuser: A Multimodal Generative Conditional Pose Prior
This addresses the problem of generating realistic human poses for 3D human modeling, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the challenge of ensuring valid SMPL configurations in 3D human pose estimation by introducing MOPED, a multi-modal conditional diffusion model as a pose prior, which significantly outperforms existing methods in pose estimation, denoising, and completion tasks.
The Skinned Multi-Person Linear (SMPL) model plays a crucial role in 3D human pose estimation, providing a streamlined yet effective representation of the human body. However, ensuring the validity of SMPL configurations during tasks such as human mesh regression remains a significant challenge , highlighting the necessity for a robust human pose prior capable of discerning realistic human poses. To address this, we introduce MOPED: \underline{M}ulti-m\underline{O}dal \underline{P}os\underline{E} \underline{D}iffuser. MOPED is the first method to leverage a novel multi-modal conditional diffusion model as a prior for SMPL pose parameters. Our method offers powerful unconditional pose generation with the ability to condition on multi-modal inputs such as images and text. This capability enhances the applicability of our approach by incorporating additional context often overlooked in traditional pose priors. Extensive experiments across three distinct tasks-pose estimation, pose denoising, and pose completion-demonstrate that our multi-modal diffusion model-based prior significantly outperforms existing methods. These results indicate that our model captures a broader spectrum of plausible human poses.