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Manga is a world popular comic form originated in Japan, which typicallyemploys black-and-white stroke lines and geometric exaggeration to describehumans' appearances, poses, and actions. In this paper, we propose MangaGAN,the first method based on Generative Adversarial Network (GAN) for unpairedphoto-to-manga translation. Inspired by how experienced manga artists drawmanga, MangaGAN generates the geometric features of manga face by a designedGAN model and delicately translates each facial region into the manga domain bya tailored multi-GANs architecture. For training MangaGAN, we construct a newdataset collected from a popular manga work, containing manga facial features,landmarks, bodies, and so on. Moreover, to produce high-quality manga faces, wefurther propose a structural smoothing loss to smooth stroke-lines and avoidnoisy pixels, and a similarity preserving module to improve the similaritybetween domains of photo and manga. Extensive experiments show that MangaGANcan produce high-quality manga faces which preserve both the facial similarityand a popular manga style, and outperforms other related state-of-the-artmethods.

MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing