  Estimating 3D human texture from a single image is essential in graphics and
vision. It requires learning a mapping function from input images of humans
with diverse poses into the parametric (UV) space and reasonably hallucinating
invisible parts. To achieve a high-quality 3D human texture estimation, we
propose a framework that adaptively samples the input by a deformable
convolution where offsets are learned via a deep neural network. Additionally,
we describe a novel cycle consistency loss that improves view generalization.
We further propose to train our framework with an uncertainty-based pixel-level
image reconstruction loss, which enhances color fidelity. We compare our method
against the state-of-the-art approaches and show significant qualitative and
quantitative improvements.
