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Authors: Masashi Aizawa ; Ryohei Orihara ; Yuichi Sei ; Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: The University of Electro-Communications, Tokyo, Japan

Keyword(s): Line Drawing, cGAN, Shading, Style Transfer.

Abstract: Coloring line drawings is an important process in creating artwork. In coloring, a shading style is where an artist’s style is the most noticeable. Artists spend a great deal of time and effort creating art. It is thus difficult for beginners to draw specific shading styles, and even experienced people have a hard time trying to draw many different styles. Features of a shading styles appear prominently in the hair region of human characters. In many cases, hair is drawn using a combination of the base color, the highlights, and the shadows. In this study, we propose a method for transferring the shading style used on hair in one drawing to another drawing. This method uses a single reference image for training and does not need a large data set. This paper describes the framework, transfer results, and discussions. The transfer results show the following: when transferring the shading style to the line drawing by the same artist, the method can detect the hair region relatively well , and the transfer result is indistinguishable from the transfer target in some shading styles. In addition, the evaluation results show that our method has higher scores than an existing automatic colorizing service. (More)

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Paper citation in several formats:
Aizawa, M.; Orihara, R.; Sei, Y.; Tahara, Y. and Ohsuga, A. (2020). Hair Shading Style Transfer for Manga with cGAN. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7; ISSN 2184-433X, pages 587-594. DOI: 10.5220/0008961405870594

@conference{icaart20,
author={Masashi Aizawa. and Ryohei Orihara. and Yuichi Sei. and Yasuyuki Tahara. and Akihiko Ohsuga.},
title={Hair Shading Style Transfer for Manga with cGAN},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={587-594},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008961405870594},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Hair Shading Style Transfer for Manga with cGAN
SN - 978-989-758-395-7
IS - 2184-433X
AU - Aizawa, M.
AU - Orihara, R.
AU - Sei, Y.
AU - Tahara, Y.
AU - Ohsuga, A.
PY - 2020
SP - 587
EP - 594
DO - 10.5220/0008961405870594