# Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2107.07943 · HTML: https://mlsanity.com/paper/2107.07943/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications](https://mlsanity.com/paper/2005.04425/) (2020)
- [Image Colorization: A Survey and Dataset](https://mlsanity.com/paper/2008.10774/) (2020)
- [Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1705.01908/) (2017)
- [cGAN-based Manga Colorization Using a Single Training Image](https://mlsanity.com/paper/1706.06918/) (2017)
- [Learning to Shadow Hand-drawn Sketches](https://mlsanity.com/paper/2002.11812/) (2020)

## Built on by (most cited first, 3 of 3)

- [Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production](https://mlsanity.com/paper/2401.03499/) (2024)
- [inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE](https://mlsanity.com/paper/2311.01804/) (2023)
- [Inference-time Trajectory Optimization for Structure-Preserving Manga Image Editing](https://mlsanity.com/paper/2603.27790/) (2026)

## Abstract (arXiv)

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.