  This work focuses on the problem of reconstructing a 3D human body mesh from
a given 2D image. Despite the inherent ambiguity of the task of human mesh
recovery, most existing works have adopted a method of regressing a single
output. In contrast, we propose a generative approach framework, called
"Diffusion-based Human Mesh Recovery (Diff-HMR)" that takes advantage of the
denoising diffusion process to account for multiple plausible outcomes. During
the training phase, the SMPL parameters are diffused from ground-truth
parameters to random distribution, and Diff-HMR learns the reverse process of
this diffusion. In the inference phase, the model progressively refines the
given random SMPL parameters into the corresponding parameters that align with
the input image. Diff-HMR, being a generative approach, is capable of
generating diverse results for the same input image as the input noise varies.
We conduct validation experiments, and the results demonstrate that the
proposed framework effectively models the inherent ambiguity of the task of
human mesh recovery in a probabilistic manner. The code is available at
https://github.com/hanbyel0105/Diff-HMR
