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We show how to learn a map that takes a content code, derived from a faceimage, and a randomly chosen style code to an anime image. We derive anadversarial loss from our simple and effective definitions of style andcontent. This adversarial loss guarantees the map is diverse -- a very widerange of anime can be produced from a single content code. Under plausibleassumptions, the map is not just diverse, but also correctly represents theprobability of an anime, conditioned on an input face. In contrast, currentmultimodal generation procedures cannot capture the complex styles that appearin anime. Extensive quantitative experiments support the idea the map iscorrect. Extensive qualitative results show that the method can generate a muchmore diverse range of styles than SOTA comparisons. Finally, we show that ourformalization of content and style allows us to perform video to videotranslation without ever training on videos.

GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)