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JOIG 2022 Vol.10(4): 145-150
doi: 10.18178/joig.10.4.145-150

Semantic Manga Character Sketch Generation

Kittinun Aukkapinyo and Seiji Hotta
Department of Computer and Information Science, Tokyo University of Agriculture and Technology, Tokyo, Japan

Abstract—A comic is one of the approaches in information and knowledge transfer. Manga is a unique and attractive comic style that originated in Japan. One of the important components in each manga is a character. It requires experience and knowledge in illustrating a manga character. Several research studies employed Generative Adversarial Networks (GANs) to illustrate a character. Although unconditional GANs could produce a high-quality image, it still lacks controllability over synthesized character. This research proposed an approach to employ conditional GANs with a semantic mask to control posture, anatomy, and basic dressing style during the synthesizing process. It also introduces an approach to systematically specify desired character’s style and parse a character into a semantic mask. As a result of a human-based evaluation, this research can competently employ a semantic mask as a condition to synthesize a decent sketch version of a manga character.

Index Terms—image generation, comic computing, deep learning

Cite: Kittinun Aukkapinyo and Seiji Hotta, "Semantic Manga Character Sketch Generation," Journal of Image and Graphics, Vol. 10, No. 4, pp. 145-150, December 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.