Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

TL;DR

This paper introduces a novel method for colorizing Fakemon images using a combination of GAN architectures, automatic color hint extraction, and specialized training for anime-style creatures, achieving promising visual results.

Contribution

It is the first to automate color hint extraction, train specifically on anime-styled Fakemon, and combine Pix2Pix and CycleGAN approaches for improved colorization.

Findings

Feasible colorization results demonstrated visually.

First to automate color hint extraction for Fakemon.

Combines two GAN architectures for enhanced results.

Abstract

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.