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In this paper, we propose a novel framework to translate a portraitphoto-face into an anime appearance. Our aim is to synthesize anime-faces whichare style-consistent with a given reference anime-face. However, unlike typicaltranslation tasks, such anime-face translation is challenging due to complexvariations of appearances among anime-faces. Existing methods often fail totransfer the styles of reference anime-faces, or introduce noticeableartifacts/distortions in the local shapes of their generated faces. We proposeAniGAN, a novel GAN-based translator that synthesizes high-quality anime-faces.Specifically, a new generator architecture is proposed to simultaneouslytransfer color/texture styles and transform local facial shapes into anime-likecounterparts based on the style of a reference anime-face, while preserving theglobal structure of the source photo-face. We propose a double-branchdiscriminator to learn both domain-specific distributions and domain-shareddistributions, helping generate visually pleasing anime-faces and effectivelymitigate artifacts. Extensive experiments on selfie2anime and a new face2animedataset qualitatively and quantitatively demonstrate the superiority of ourmethod over state-of-the-art methods. The new dataset is available athttps://github.com/bing-li-ai/AniGAN .

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation