Automatic manga colorization with color style by generative adversarial nets

Abstract

Many comic books are now published as digital books, which easily provide color contents compared to the physical books. The motivation of automatic colorization of comic books now arises. Previous studies colorize sketches without other clues or with spatial color annotations. They are expected to reduce workloads of comic artists but still require spatial color annotations for desirable colorizations. This study introduces a color style information and combines it with conditional adversarially learned inference. The experimental results demonstrate that the objects are painted with colors depending on the color style information and that the color style information extracted from a color image supports to painting an object with a desirable color.