  Single-image 3D human reconstruction aims to reconstruct the 3D textured
surface of the human body given a single image. While implicit function-based
methods recently achieved reasonable reconstruction performance, they still
bear limitations showing degraded quality in both surface geometry and texture
from an unobserved view. In response, to generate a realistic textured surface,
we propose ReFu, a coarse-to-fine approach that refines the projected backside
view image and fuses the refined image to predict the final human body. To
suppress the diffused occupancy that causes noise in projection images and
reconstructed meshes, we propose to train occupancy probability by
simultaneously utilizing 2D and 3D supervisions with occupancy-based volume
rendering. We also introduce a refinement architecture that generates
detail-preserving backside-view images with front-to-back warping. Extensive
experiments demonstrate that our method achieves state-of-the-art performance
in 3D human reconstruction from a single image, showing enhanced geometry and
texture quality from an unobserved view.
