  Unsupervised image-to-image translation techniques are able to map local
texture between two domains, but they are typically unsuccessful when the
domains require larger shape change. Inspired by semantic segmentation, we
introduce a discriminator with dilated convolutions that is able to use
information from across the entire image to train a more context-aware
generator. This is coupled with a multi-scale perceptual loss that is better
able to represent error in the underlying shape of objects. We demonstrate that
this design is more capable of representing shape deformation in a challenging
toy dataset, plus in complex mappings with significant dataset variation
between humans, dolls, and anime faces, and between cats and dogs.
