  In this paper, we address the problem of texture representation for 3D shapes
for the challenging and underexplored tasks of texture transfer and synthesis.
Previous works either apply spherical texture maps which may lead to large
distortions, or use continuous texture fields that yield smooth outputs lacking
details. We argue that the traditional way of representing textures with images
and linking them to a 3D mesh via UV mapping is more desirable, since
synthesizing 2D images is a well-studied problem. We propose AUV-Net which
learns to embed 3D surfaces into a 2D aligned UV space, by mapping the
corresponding semantic parts of different 3D shapes to the same location in the
UV space. As a result, textures are aligned across objects, and can thus be
easily synthesized by generative models of images. Texture alignment is learned
in an unsupervised manner by a simple yet effective texture alignment module,
taking inspiration from traditional works on linear subspace learning. The
learned UV mapping and aligned texture representations enable a variety of
applications including texture transfer, texture synthesis, and textured single
view 3D reconstruction. We conduct experiments on multiple datasets to
demonstrate the effectiveness of our method. Project page:
https://nv-tlabs.github.io/AUV-NET.
