Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks

TL;DR

This paper introduces a semi-automatic GAN-based method for consistent manga colorization that learns a specific comic's painting style from limited data, improving over existing methods.

Contribution

It presents a novel style-aware manga colorization approach using GANs that maintains consistency across multiple images with minimal training data.

Findings

Outperforms existing colorization methods in experiments.

Achieves style consistency across manga pages.

Requires only small amounts of training data.

Abstract

Japanese comics (called manga) are traditionally created in monochrome format. In recent years, in addition to monochrome comics, full color comics, a more attractive medium, have appeared. Unfortunately, color comics require manual colorization, which incurs high labor costs. Although automatic colorization methods have been recently proposed, most of them are designed for illustrations, not for comics. Unlike illustrations, since comics are composed of many consecutive images, the painting style must be consistent. To realize consistent colorization, we propose here a semi-automatic colorization method based on generative adversarial networks (GAN); the method learns the painting style of a specific comic from small amount of training data. The proposed method takes a pair of a screen tone image and a flat colored image as input, and outputs a colorized image. Experiments show that the proposed method achieves better performance than the existing alternatives.