Recovering a 3D human mesh from a single RGB image is a challenging task due to depth ambiguity and self-occlusion, resulting in a high degree of uncertainty. Meanwhile, diffusion models have recently seen much success in generating high-quality outputs by progressively denoising noisy inputs. Inspired by their capability, we explore a diffusion-based approach for human mesh recovery, and propose a Human Mesh Diffusion (HMDiff) framework which frames mesh recovery as a reverse diffusion process. We also propose a Distribution Alignment Technique (DAT) that infuses prior distribution information into the mesh distribution diffusion process, and provides useful prior knowledge to facilitate the mesh recovery task. Our method achieves state-of-the-art performance on three widely used datasets. Project page: https://gongjia0208.github.io/HMDiff/.
@article{arxiv.2308.13369,
title = {Distribution-Aligned Diffusion for Human Mesh Recovery},
author = {Lin Geng Foo and Jia Gong and Hossein Rahmani and Jun Liu},
journal= {arXiv preprint arXiv:2308.13369},
year = {2023}
}