  Recent advances in differentiable rendering have sparked an interest in
learning generative models of textured 3D meshes from image collections. These
models natively disentangle pose and appearance, enable downstream applications
in computer graphics, and improve the ability of generative models to
understand the concept of image formation. Although there has been prior work
on learning such models from collections of 2D images, these approaches require
a delicate pose estimation step that exploits annotated keypoints, thereby
restricting their applicability to a few specific datasets. In this work, we
propose a GAN framework for generating textured triangle meshes without relying
on such annotations. We show that the performance of our approach is on par
with prior work that relies on ground-truth keypoints, and more importantly, we
demonstrate the generality of our method by setting new baselines on a larger
set of categories from ImageNet - for which keypoints are not available -
without any class-specific hyperparameter tuning.
