Generative Approach for Probabilistic Human Mesh Recovery using Diffusion Models
This addresses the ambiguity in human mesh recovery for computer vision applications, offering a probabilistic alternative to single-output regression methods.
The paper tackles the problem of reconstructing a 3D human body mesh from a 2D image by proposing a generative approach called Diff-HMR, which uses diffusion models to account for multiple plausible outcomes, effectively modeling the inherent ambiguity of the task in a probabilistic manner.
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