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.
Copyright © 2012-2023 Journal of Image and Graphics, All Rights Reserved