Colorization for Anime Sketches with Cycle-Consistent Adversarial Network

Published in International Journal Performability Engineering, 2019

Coloring animation sketches has always been a complex and interesting task, but as the sketch is the first part of animation creation that neither presents gray value nor presents semantic information, the lack of real animation sketches is the biggest difficulty in current model training. It is also usually expensive to collect such data. In recent years, some methods based on generative adversarial networks (GANs) have achieved great success. They can generate colorized anime illustration on given sketches…

Recommended citation: Guanghua Zhang, Mengnan Qu, Yuhao Jin, Qingpeng Song. "Colorization for Anime Sketches with Cycle Consistent Adversarial Network." International Journal of Performability Engineering 15.3(2019):910-918.