Closing the Domain Gap in Manga Colorization via Aligned Paired Dataset

Maksim Golyadkin, Ianis Plevokas, Ilya Makarov; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5580-5590

Abstract


This paper addresses the challenge of artwork colorization by proposing a benchmark for manga colorization using real black-and-white and colorized image pairs. Color images are widely recognized for their ability to capture attention and improve memory retention yet the manual process of colorization is labor-intensive. Deep learning methods for supervised image-to-image translation offer a promising solution relying on aligned pairs of black-and-white and color images for training. However these pairs are often generated synthetically introducing a domain gap that limits model performance. To address this we explore the use of real data proposing a method for creating such datasets. Our benchmarks reveal that models trained on real data significantly outperform those trained on synthetic pairs. Furthermore we present a pipeline for text removal and panel segmentation streamlining the comic colorization process. These contributions aim to enhance the generalization and applicability of deep learning models for artwork colorization.

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[bibtex]
@InProceedings{Golyadkin_2025_WACV, author = {Golyadkin, Maksim and Plevokas, Ianis and Makarov, Ilya}, title = {Closing the Domain Gap in Manga Colorization via Aligned Paired Dataset}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {5580-5590} }