Anime Character Colorization using Few-shot Learning
Akinobu Maejima, Hiroyuki Kubo, Seitaro Shinagawa, Takuya Funatomi, Tatsuo Yotsukura, Satoshi Nakamura, Yasuhiro Mukaigawa · 2021
In this paper, we propose an automatic Anime-style colorization method using only a small number of colorized reference images manually colorized by artists. To accomplish this, we introduce a few-shot patch-based learning method considering the characteristics of Anime line-drawing. To streamline the learning process, we derive optimal settings with acceptable colorization accuracy and training time for a production pipeline. We demonstrate that the proposed method helps to reduce manual labor for artists.