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Painting Style-Aware Manga Colorization Based On Generative Adversarial Networks

机译：绘画风格感知漫画彩色基于生成对抗网络

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摘要

Japanese comics (called manga) are traditionally created in monochrome format. In recent years, in addition to monochrome comics, full color comics, a more attractive medium, have appeared. Unfortunately, color comics require manual colorization, which incurs high labor costs. Although automatic colorization methods have been recently proposed, most of them are designed for illustrations, not for comics. Unlike illustrations, since comics are composed of many consecutive images, the painting style must be consistent. To realize consistent colorization, we propose here a semi-automatic colorization method based on generative adversarial networks (GAN); the method learns the painting style of a specific comic from small amount of training data. The proposed method takes a pair of a screen tone image and a flat colored image as input, and outputs a colorized image. Experiments show that the proposed method achieves better performance than the existing alternatives.
机译：日本漫画（称为漫画）传统上以单色格式创建。 近年来，除了单色漫画，全彩色漫画，更具吸引力的媒介，出现了。 不幸的是，颜色漫画需要手动着色，从而引发高劳动力成本。 尽管最近已经提出了自动彩色方法，但大多数都是针对插图设计的，而不是漫画。 与插图不同，由于漫画由许多连续图像组成，因此绘画样式必须是一致的。 为了实现一致的着色，我们在此提出了一种基于生成对冲网络（GaN）的半自动着色方法; 该方法从少量训练数据中了解特定漫画的绘画样式。 该方法采用一对屏幕音调图像和平面彩色图像作为输入，并输出彩色图像。 实验表明，该方法的性能比现有替代品更好。

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