# Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

arXiv: https://arxiv.org/abs/2307.05760 · HTML: https://mlsanity.com/paper/2307.05760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Real-Time User-Guided Image Colorization with Learned Deep Priors](https://mlsanity.com/paper/1705.02999/) (2017)
- [User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks](https://mlsanity.com/paper/1808.03240/) (2018)
- [Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention](https://mlsanity.com/paper/2104.14374/) (2021)
- [Morphological classifiers](https://mlsanity.com/paper/2112.12262/) (2021)
- [k-MS: A novel clustering algorithm based on morphological reconstruction](https://mlsanity.com/paper/2208.14390/) (2022)
- [Automated recognition of the pericardium contour on processed CT images using genetic algorithms](https://mlsanity.com/paper/2208.14375/) (2022)

## Built on by (most cited first, 1 of 1)

- [inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE](https://mlsanity.com/paper/2311.01804/) (2023)

## Abstract (arXiv)

This work proposes a complete methodology to colorize images of Fakemon, anime-style monster-like creatures. In addition, we propose algorithms to extract the line art from colorized images as well as to extract color hints. Our work is the first in the literature to use automatic color hint extraction, to train the networks specifically with anime-styled creatures and to combine the Pix2Pix and CycleGAN approaches, two different generative adversarial networks that create a single final result. Visual results of the colorizations are feasible but there is still room for improvement.