  We train generative 'up-convolutional' neural networks which are able to
generate images of objects given object style, viewpoint, and color. We train
the networks on rendered 3D models of chairs, tables, and cars. Our experiments
show that the networks do not merely learn all images by heart, but rather find
a meaningful representation of 3D models allowing them to assess the similarity
of different models, interpolate between given views to generate the missing
ones, extrapolate views, and invent new objects not present in the training set
by recombining training instances, or even two different object classes.
Moreover, we show that such generative networks can be used to find
correspondences between different objects from the dataset, outperforming
existing approaches on this task.
