Training Cartoonization Network without Cartoon

Doyeon Kim, Dongyeun Lee, Donggyu Joo, Junmo Kim

Abstract: Photo cartoonization aims to translate a real-world photo into a cartoon image. Previous learning-based cartoonization studies have shown promising results, however, an alternative approach is still necessary because of the following limitations. First, the deficiency of training datasets necessitates the additional dataset acquisition process, which yields uneven network training results. Second, we observe that the created images using existing works have color transition problems. In this paper, we propose a new approach on photo cartoonization which does not use cartoon datasets and does not suffer these limitations. By focusing on the two important aspects of cartoon style, regions and edges, our work enables us to produce cartoonized images without using a manually collected dataset. We show that our framework can generate high-quality cartoonized images and achieve competitive performance with comparable cartoonization networks even without training cartoon dataset.