  Japanese comics (called manga) are traditionally created in monochrome
format. In recent years, in addition to monochrome comics, full color comics, a
more attractive medium, have appeared. Unfortunately, color comics require
manual colorization, which incurs high labor costs. Although automatic
colorization methods have been recently proposed, most of them are designed for
illustrations, not for comics. Unlike illustrations, since comics are composed
of many consecutive images, the painting style must be consistent. To realize
consistent colorization, we propose here a semi-automatic colorization method
based on generative adversarial networks (GAN); the method learns the painting
style of a specific comic from small amount of training data. The proposed
method takes a pair of a screen tone image and a flat colored image as input,
and outputs a colorized image. Experiments show that the proposed method
achieves better performance than the existing alternatives.
