Line Art Colorization of Fakemon using Generative Adversarial Neural Networks
This work addresses colorization for anime-style creature art, but it is incremental as it adapts existing methods to a specific domain.
The paper tackles the problem of colorizing line art images of Fakemon, anime-style creatures, by combining Pix2Pix and CycleGAN approaches, resulting in feasible visual colorizations with room for improvement.
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.