  Humans can easily infer the underlying 3D geometry and texture of an object
only from a single 2D image. Current computer vision methods can do this, too,
but suffer from view generalization problems - the models inferred tend to make
poor predictions of appearance in novel views. As for generalization problems
in machine learning, the difficulty is balancing single-view accuracy (cf.
training error; bias) with novel view accuracy (cf. test error; variance). We
describe a class of models whose geometric rigidity is easily controlled to
manage this tradeoff. We describe a cycle consistency loss that improves view
generalization (roughly, a model from a generated view should predict the
original view well). View generalization of textures requires that models share
texture information, so a car seen from the back still has headlights because
other cars have headlights. We describe a cycle consistency loss that
encourages model textures to be aligned, so as to encourage sharing. We compare
our method against the state-of-the-art method and show both qualitative and
quantitative improvements.
