AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Bing Li, Yuanlue Zhu, Yitong Wang, Chia‐Wen Lin, Bernard Ghanem, Linlin Shen
Abstract
In this paper, we propose a novel framework to translate a portrait photo-face into an anime appearance. Different from existing translation methods which do not designate specific styles, we aim to synthesize anime-faces which are style-consistent with a given reference anime-face. However, unlike typical translation tasks, such anime-face translation is particularly challenging due to the large and complex variations of appearances among anime-faces. Existing methods often fail to transfer the styles of reference anime-faces to the generated anime-faces, or introduce noticeable artifacts/distortions in the local shapes of their generated anime-faces. We propose a novel GAN-based anime-face translator, called AniGAN, to synthesize 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. New normalization functions are designed for the generator to further improve local shape transformation and color/texture style transfer. Besides, we propose a double-branch discriminator to learn domain-specific distributions through individual branches and learn cross-domain shared distributions via shared layers, helping generate visually pleasing anime-faces and effectively mitigate artifacts/distortions. Extensive experiments on benchmark datasets qualitatively and quantitatively demonstrate the superiority of our method over state-of-the-art methods.