  We introduce a novel approach to generate diverse high fidelity texture maps
for 3D human meshes in a semi-supervised setup. Given a segmentation mask
defining the layout of the semantic regions in the texture map, our network
generates high-resolution textures with a variety of styles, that are then used
for rendering purposes. To accomplish this task, we propose a Region-adaptive
Adversarial Variational AutoEncoder (ReAVAE) that learns the probability
distribution of the style of each region individually so that the style of the
generated texture can be controlled by sampling from the region-specific
distributions. In addition, we introduce a data generation technique to augment
our training set with data lifted from single-view RGB inputs. Our training
strategy allows the mixing of reference image styles with arbitrary styles for
different regions, a property which can be valuable for virtual try-on AR/VR
applications. Experimental results show that our method synthesizes better
texture maps compared to prior work while enabling independent layout and style
controllability.
