  We present a learning framework that learns to recover the 3D shape, pose and
texture from a single image, trained on an image collection without any ground
truth 3D shape, multi-view, camera viewpoints or keypoint supervision. We
approach this highly under-constrained problem in a "analysis by synthesis"
framework where the goal is to predict the likely shape, texture and camera
viewpoint that could produce the image with various learned category-specific
priors. Our particular contribution in this paper is a representation of the
distribution over cameras, which we call "camera-multiplex". Instead of picking
a point estimate, we maintain a set of camera hypotheses that are optimized
during training to best explain the image given the current shape and texture.
We call our approach Unsupervised Category-Specific Mesh Reconstruction
(U-CMR), and present qualitative and quantitative results on CUB, Pascal 3D and
new web-scraped datasets. We obtain state-of-the-art camera prediction results
and show that we can learn to predict diverse shapes and textures across
objects using an image collection without any keypoint annotations or 3D ground
truth. Project page: https://shubham-goel.github.io/ucmr
