  In this paper, we propose a novel framework to translate a portrait
photo-face into an anime appearance. Our aim is to synthesize anime-faces which
are style-consistent with a given reference anime-face. However, unlike typical
translation tasks, such anime-face translation is challenging due to complex
variations of appearances among anime-faces. Existing methods often fail to
transfer the styles of reference anime-faces, or introduce noticeable
artifacts/distortions in the local shapes of their generated faces. We propose
AniGAN, a novel GAN-based translator that synthesizes high-quality anime-faces.
Specifically, a new generator architecture is proposed to simultaneously
transfer color/texture styles and transform local facial shapes into anime-like
counterparts based on the style of a reference anime-face, while preserving the
global structure of the source photo-face. We propose a double-branch
discriminator to learn both domain-specific distributions and domain-shared
distributions, helping generate visually pleasing anime-faces and effectively
mitigate artifacts. Extensive experiments on selfie2anime and a new face2anime
dataset qualitatively and quantitatively demonstrate the superiority of our
method over state-of-the-art methods. The new dataset is available at
https://github.com/bing-li-ai/AniGAN .
