Line Art Colorization of Fakemon using Generative Adversarial Neural
  Networks
Erick Oliveira Rodrigues Esteban Clua Giovani Bernardes Vitor
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.