# GANILLA: Generative Adversarial Networks for Image to Illustration Translation

arXiv: https://arxiv.org/abs/2002.05638 · HTML: https://mlsanity.com/paper/2002.05638/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [Improving Sketch Colorization using Adversarial Segmentation Consistency](https://mlsanity.com/paper/2301.08590/) (2023)
- [Adversarial Segmentation Loss for Sketch Colorization](https://mlsanity.com/paper/2102.06192/) (2021)
- [MISS GAN: A Multi-IlluStrator Style Generative Adversarial Network for image to illustration translation](https://mlsanity.com/paper/2108.05693/) (2021)
- [Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training](https://mlsanity.com/paper/2305.07613/) (2023)
- [Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings](https://mlsanity.com/paper/2211.10641/) (2022)
- [Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2107.02494/) (2021)
- [Neural Style Transfer and Unpaired Image-to-Image Translation to deal with the Domain Shift Problem on Spheroid Segmentation](https://mlsanity.com/paper/2112.09043/) (2021)
- [WAIT: Feature Warping for Animation to Illustration video Translation using GANs](https://mlsanity.com/paper/2310.04901/) (2023)
- [NSYNC: Negative Synthetic Image Generation for Contrastive Training to Improve Stylized Text-To-Image Translation](https://mlsanity.com/paper/2511.01517/) (2025)
- [Abstract-LoRA: Unlocking Single-Image Style Transfer through Targeted U-Net Block Training](https://mlsanity.com/paper/2609.13239/) (2026)

## Abstract (arXiv)

In this paper, we explore illustrations in children's books as a new domain in unpaired image-to-image translation. We show that although the current state-of-the-art image-to-image translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content. There are no well-defined or agreed-upon evaluation metrics for unpaired image-to-image translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of image-to-illustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current state-of-the-art models on the illustrations dataset. Our code and pretrained models can be found at https://github.com/giddyyupp/ganilla.