“Predicting Colors in Unpainted Gaps for Anime-Style Illustration” by Kono, Maejima, Koyama and Igarashi
Conference:
Type(s):
Title:
- Predicting Colors in Unpainted Gaps for Anime-Style Illustration
Session/Category Title:
- Images, Video & Computer Vision
Presenter(s)/Author(s):
Abstract:
We introduce the novel task of predicting flat colors for unintended small regions left unpainted by flood-fill operations—common in anime-style illustrations—and present a U-Net-based method that achieves 62.5% exact-match accuracy on professional data, outperforming naïve baselines and establishing a promising foundation for supporting anime-style colorization workflows.
References:
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[4] Kazuhiro Sato, Yusuke Matsui, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2014. Reference-based manga colorization by graph correspondence using quadratic programming. In SIGGRAPH Asia 2014 Technical Briefs. 1–4.
[5] Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf. 2022. Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers.
[6] Xiaobo Zhang, Donghai Zhai, Tianrui Li, Yuxin Zhou, and Yang Lin. 2023. Image inpainting based on deep learning: A review. Information Fusion 90 (2023), 74–94.
