  The recovery of occluded human meshes presents challenges for current methods
due to the difficulty in extracting effective image features under severe
occlusion. In this paper, we introduce DPMesh, an innovative framework for
occluded human mesh recovery that capitalizes on the profound diffusion prior
about object structure and spatial relationships embedded in a pre-trained
text-to-image diffusion model. Unlike previous methods reliant on conventional
backbones for vanilla feature extraction, DPMesh seamlessly integrates the
pre-trained denoising U-Net with potent knowledge as its image backbone and
performs a single-step inference to provide occlusion-aware information. To
enhance the perception capability for occluded poses, DPMesh incorporates
well-designed guidance via condition injection, which produces effective
controls from 2D observations for the denoising U-Net. Furthermore, we explore
a dedicated noisy key-point reasoning approach to mitigate disturbances arising
from occlusion and crowded scenarios. This strategy fully unleashes the
perceptual capability of the diffusion prior, thereby enhancing accuracy.
Extensive experiments affirm the efficacy of our framework, as we outperform
state-of-the-art methods on both occlusion-specific and standard datasets. The
persuasive results underscore its ability to achieve precise and robust 3D
human mesh recovery, particularly in challenging scenarios involving occlusion
and crowded scenes.
