arXiv Open Access 2023

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

Erick Oliveira Rodrigues Esteban Clua Giovani Bernardes Vitor

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Abstrak

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.

Topik & Kata Kunci

cs.CV

Penulis (3)

Erick Oliveira Rodrigues

Esteban Clua

Giovani Bernardes Vitor

Format Sitasi

APA MLA BibTeX

Rodrigues, E.O., Clua, E., Vitor, G.B. (2023). Line Art Colorization of Fakemon using Generative Adversarial Neural Networks. https://arxiv.org/abs/2307.05760

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓