  We show how to learn a map that takes a content code, derived from a face
image, and a randomly chosen style code to an anime image. We derive an
adversarial loss from our simple and effective definitions of style and
content. This adversarial loss guarantees the map is diverse -- a very wide
range of anime can be produced from a single content code. Under plausible
assumptions, the map is not just diverse, but also correctly represents the
probability of an anime, conditioned on an input face. In contrast, current
multimodal generation procedures cannot capture the complex styles that appear
in anime. Extensive quantitative experiments support the idea the map is
correct. Extensive qualitative results show that the method can generate a much
more diverse range of styles than SOTA comparisons. Finally, we show that our
formalization of content and style allows us to perform video to video
translation without ever training on videos.
