DiffProxy: Multi-View Human Mesh Recovery via Diffusion-Generated Dense Proxies

Abstract

Precise human mesh recovery (HMR) from multi-view im-ages remains challenging: end-to-end methods produce entangled errorshard to localize, while _x001C_tting-based methods rely on sparse keypointsthat provide limited surface constraints. We observe that the true bot-tleneck lies in the quality of intermediate representations, and that densepixel-to-surface correspondences can be e_x001B_ectively generated by repur-posing pre-trained di_x001B_usion models with rich visual priors. We proposeDi_x001B_Proxy, a Stable-Di_x001B_usion-based framework trained on large-scalesynthetic data with pixel-perfect annotations. A multi-conditional proxygenerator predicts dense correspondences from multi-view images, pro-viding uniform surface constraints that enable precise _x001C_tting. Hand re-_x001C_nement feeds enlarged hand crops alongside full-body images for _x001C_ne-grained detail, while test-time scaling exploits di_x001B_usion stochasticity toestimate per-pixel uncertainty. Trained only on synthetic data, Di_x001B_Proxyachieves state-of-the-art results on _x001C_ve diverse real-world benchmarks.Project page: https://wrk226.github.io/DiffProxy.html