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Reconstructing 3D humans from a single image has been extensivelyinvestigated. However, existing approaches often fall short on capturing finegeometry and appearance details, hallucinating occluded parts with plausibledetails, and achieving generalization across unseen and in-the-wild datasets.We present Human-LRM, a diffusion-guided feed-forward model that predicts theimplicit field of a human from a single image. Leveraging the power of thestate-of-the-art reconstruction model (i.e., LRM) and generative model (i.eStable Diffusion), our method is able to capture human without any templateprior, e.g., SMPL, and effectively enhance occluded parts with rich andrealistic details. Our approach first uses a single-view LRM model with anenhanced geometry decoder to get the triplane NeRF representation. The novelview renderings from the triplane NeRF provide strong geometry and color prior,from which we generate photo-realistic details for the occluded parts using adiffusion model. The generated multiple views then enable reconstruction withhigh-quality geometry and appearance, leading to superior overall performancecomparing to all existing human reconstruction methods.

Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM