  While recent generative models for 2D images achieve impressive visual
results, they clearly lack the ability to perform 3D reasoning. This heavily
restricts the degree of control over generated objects as well as the possible
applications of such models. In this work, we bridge this gap by leveraging
recent advances in differentiable rendering. We design a framework that can
generate triangle meshes and associated high-resolution texture maps, using
only 2D supervision from single-view natural images. A key contribution of our
work is the encoding of the mesh and texture as 2D representations, which are
semantically aligned and can be easily modeled by a 2D convolutional GAN. We
demonstrate the efficacy of our method on Pascal3D+ Cars and CUB, both in an
unconditional setting and in settings where the model is conditioned on class
labels, attributes, and text. Finally, we propose an evaluation methodology
that assesses the mesh and texture quality separately.
