anywherefor.blogg.se

We describe a cycle consistency loss that encourages model textures to be aligned, so as to encourage sharing.

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 improves view generalization (roughly, a model from a generated view should predict the original view well). We describe a class of models whose geometric rigidity is easily controlled to manage this tradeoff.

training error bias) with novel view accuracy (cf. As for generalization problems in machine learning, the difficulty is balancing single-view accuracy (cf. 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. Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image.