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

• URL: http://arxiv.org/abs/2102.12593v1
• Date: Wed, 24 Feb 2021 22:47:38 GMT
• Title: AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
• Authors: Bing Li, Yuanlue Zhu, Yitong Wang, Chia-Wen Lin, Bernard Ghanem, Linlin Shen
• Abstract summary: 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. 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.
• Score: 84.52819242283852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Ani- GAN, a novel GAN-based translator that synthesizes highquality 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 photoface. 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 qualitatively and quantitatively demonstrate the superiority of our method over state-of-the-art methods.

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