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/.

翻译：从单张RGB图像中恢复3D人体网格是一项具有挑战性的任务，这是因为深度模糊和自遮挡会导致高度不确定性。与此同时，扩散模型最近在通过逐步去噪噪声输入生成高质量输出方面取得了显著成功。受此能力的启发，我们探索了一种基于扩散的人体网格恢复方法，并提出了人体网格扩散（HMDiff）框架，该框架将网格恢复视为反向扩散过程。我们还提出了一种分布对齐技术（DAT），该技术将先验分布信息注入网格分布扩散过程，并提供有用的先验知识以促进网格恢复任务。我们的方法在三个广泛使用的数据集上达到了最先进的性能。项目页面：https://gongjia0208.github.io/HMDiff/。