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http://dx.doi.org/10.36498/kbigdt.2022.7.1.173

A Study on Webtoon Background Image Generation Using CartoonGAN Algorithm  

Saekyu Oh (아주대학교 비즈니스애널리틱스학과)
Juyoung Kang (아주대학교 비즈니스애널리틱스학과)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 173-185 More about this Journal
Abstract
Nowadays, Korean webtoons are leading the global digital comic market. Webtoons are being serviced in various languages around the world, and dramas or movies produced with Webtoons' IP (Intellectual Property Rights) have become a big hit, and more and more webtoons are being visualized. However, with the success of these webtoons, the working environment of webtoon creators is emerging as an important issue. According to the 2021 Cartoon User Survey, webtoon creators spend 10.5 hours a day on creative activities on average. Creators have to draw large amount of pictures every week, and competition among webtoons is getting fiercer, and the amount of paintings that creators have to draw per episode is increasing. Therefore, this study proposes to generate webtoon background images using deep learning algorithms and use them for webtoon production. The main character in webtoon is an area that needs much of the originality of the creator, but the background picture is relatively repetitive and does not require originality, so it can be useful for webtoon production if it can create a background picture similar to the creator's drawing style. Background generation uses CycleGAN, which shows good performance in image-to-image translation, and CartoonGAN, which is specialized in the Cartoon style image generation. This deep learning-based image generation is expected to shorten the working hours of creators in an excessive work environment and contribute to the convergence of webtoons and technologies.
Keywords
Webtoon; GANs(Generative Adversarial Networks); CycleGAN; CartoonGAN; image-to-image translation; webtoon production;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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