Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks

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 col...

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Bibliographic Details
Published inarXiv.org
Main Authors Shimizu, Yugo, Furuta, Ryosuke, Delong Ouyang, Taniguchi, Yukinobu, Hinami, Ryota, Ishiwatari, Shonosuke
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.07.2021
Subjects
Color
Colorization
Comics
Generative adversarial networks
Illustrations
Manga
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Summary: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.
ISSN:2331-8422