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Semi-automatic Manga Colorization Using Conditional Adversarial Networks

P. 230-242.
Maksim Golyadkin, Makarov I.

Manga colorization is time-consuming and hard to automate. In this paper, we propose a conditional adversarial deep learning approach for semi-automatic manga images colorization. The system directly maps a tuple of grayscale manga page image and sparse color hint constructed by the user to an output colorization. High-quality colorization can be obtained in a fully automated way, and color hints allow users to revise the colorization of every panel independently. We collect a dataset of manually colorized and grayscale manga images for training and evaluation. To perform supervised learning, we construct synthesized monochrome images from colorized. Furthermore, we suggest a few steps to reduce the domain gap between synthetic and real data. Their influence is evaluated both quantitatively and qualitatively. Our method can achieve even better results by fine-tuning with a small number of grayscale manga images of a new style. The code is available at github.com.

В книге

Под науч. редакцией: W. M. van der Aalst, V. Batagelj, D. I. Ignatov et al. Vol. 12602. Springer, 2021.