Generating 3D Human Texture from a Single Image with Sampling and Refinement

Abstract

Generating the texture map for a 3D human mesh from a single image is challenging. To generate a plausible texture map, the invisible parts of the texture need to be synthesized with relevance to the visible part and the texture should semantically align to the UV space of the template mesh. To overcome such challenges, we propose a novel method that incorporates SamplerNet and RefineNet. SamplerNet predicts a sampling grid that enables sampling from the given visible texture information, and RefineNet refines the sampled texture to maintain spatial alignment.