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Single-image 3D human reconstruction aims to reconstruct the 3D texturedsurface of the human body given a single image. While implicit function-basedmethods recently achieved reasonable reconstruction performance, they stillbear limitations showing degraded quality in both surface geometry and texturefrom an unobserved view. In response, to generate a realistic textured surface,we propose ReFu, a coarse-to-fine approach that refines the projected backsideview image and fuses the refined image to predict the final human body. Tosuppress the diffused occupancy that causes noise in projection images andreconstructed meshes, we propose to train occupancy probability bysimultaneously utilizing 2D and 3D supervisions with occupancy-based volumerendering. We also introduce a refinement architecture that generatesdetail-preserving backside-view images with front-to-back warping. Extensiveexperiments demonstrate that our method achieves state-of-the-art performancein 3D human reconstruction from a single image, showing enhanced geometry andtexture quality from an unobserved view.

ReFu: Refine and Fuse the Unobserved View for Detail-Preserving Single-Image 3D Human Reconstruction