  We learn a self-supervised, single-view 3D reconstruction model that predicts
the 3D mesh shape, texture and camera pose of a target object with a collection
of 2D images and silhouettes. The proposed method does not necessitate 3D
supervision, manually annotated keypoints, multi-view images of an object or a
prior 3D template. The key insight of our work is that objects can be
represented as a collection of deformable parts, and each part is semantically
coherent across different instances of the same category (e.g., wings on birds
and wheels on cars). Therefore, by leveraging self-supervisedly learned part
segmentation of a large collection of category-specific images, we can
effectively enforce semantic consistency between the reconstructed meshes and
the original images. This significantly reduces ambiguities during joint
prediction of shape and camera pose of an object, along with texture. To the
best of our knowledge, we are the first to try and solve the single-view
reconstruction problem without a category-specific template mesh or semantic
keypoints. Thus our model can easily generalize to various object categories
without such labels, e.g., horses, penguins, etc. Through a variety of
experiments on several categories of deformable and rigid objects, we
demonstrate that our unsupervised method performs comparably if not better than
existing category-specific reconstruction methods learned with supervision.
