  Manga is a world popular comic form originated in Japan, which typically
employs black-and-white stroke lines and geometric exaggeration to describe
humans' appearances, poses, and actions. In this paper, we propose MangaGAN,
the first method based on Generative Adversarial Network (GAN) for unpaired
photo-to-manga translation. Inspired by how experienced manga artists draw
manga, MangaGAN generates the geometric features of manga face by a designed
GAN model and delicately translates each facial region into the manga domain by
a tailored multi-GANs architecture. For training MangaGAN, we construct a new
dataset collected from a popular manga work, containing manga facial features,
landmarks, bodies, and so on. Moreover, to produce high-quality manga faces, we
further propose a structural smoothing loss to smooth stroke-lines and avoid
noisy pixels, and a similarity preserving module to improve the similarity
between domains of photo and manga. Extensive experiments show that MangaGAN
can produce high-quality manga faces which preserve both the facial similarity
and a popular manga style, and outperforms other related state-of-the-art
methods.
