Generation and Analysis of Webtoon Background Images Using GAN

Abstract

In this paper, we propose a method to reduce the amount of manual work in webtoon creation and utilize creative contents derived from AI learning through deep learning-based technology that generates background images of various styles. To achieve this goal, we train CartoonGAN and AnimeGAN models that are specialized in creating images in the style of webtoons and animations, and create background images that can be used for webtoons. Recently, various Generative Adversarial Network (GAN) models have been actively used to create digital content, but cartoon-style images should be created with simplified textures and sharp outlines. In addition, when converting a real image into a cartoon style, it is necessary to create a simple and abstract image while maintaining the content expressed by the image. We build training data suitable for the production of these webtoon-style images, and analyze whether the images generated by the two GAN models can be used for webtoon production, and seek ways to utilize generative AI.