# MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing

arXiv: https://arxiv.org/abs/2004.10634 · HTML: https://mlsanity.com/paper/2004.10634/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation](https://mlsanity.com/paper/2110.11728/) (2021)
- [DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation](https://mlsanity.com/paper/2412.07589/) (2024)
- [Unsupervised Manga Character Re-identification via Face-body and Spatial-temporal Associated Clustering](https://mlsanity.com/paper/2204.04621/) (2022)
- [WISE: Whitebox Image Stylization by Example-based Learning](https://mlsanity.com/paper/2207.14606/) (2022)
- [DiffFaceSketch: High-Fidelity Face Image Synthesis with Sketch-Guided Latent Diffusion Model](https://mlsanity.com/paper/2302.06908/) (2023)
- [Fix the Noise: Disentangling Source Feature for Controllable Domain Translation](https://mlsanity.com/paper/2303.11545/) (2023)
- [Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production](https://mlsanity.com/paper/2401.03499/) (2024)
- [MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning](https://mlsanity.com/paper/2110.04830/) (2021)
- [FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer](https://mlsanity.com/paper/2307.09020/) (2023)

## Abstract (arXiv)

Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by how experienced manga artists draw manga, MangaGAN generates the geometric features of manga face by a designed GAN model and delicately translates each facial region into the manga domain by a tailored multi-GANs architecture. For training MangaGAN, we construct a new dataset collected from a popular manga work, containing manga facial features, landmarks, bodies, and so on. Moreover, to produce high-quality manga faces, we further propose a structural smoothing loss to smooth stroke-lines and avoid noisy pixels, and a similarity preserving module to improve the similarity between domains of photo and manga. Extensive experiments show that MangaGAN can produce high-quality manga faces which preserve both the facial similarity and a popular manga style, and outperforms other related state-of-the-art methods.