基于条件生成对抗网络的漫画手绘图上色方法

Colorization of manga sketch based on conditional generative adversarial networks

提出一种基于条件生成对抗网络(CGAN)的漫画手绘图自动上色方法。实验中,采用U型结构的生成器,对网络模型使用L1进行约束,在生成器和判别器的对抗式训练中,模型不断学习并优化手绘图到对应彩色图像间的映射关系,最后使用训练得到的条件GAN网络模型对手绘图上色。实验表明,使用这种方法可以有效并且快速地对漫画手绘图上色,同时保持可观的视觉效果。

This paper proposed a method to color manga sketch in unsupervised based conditional generative adversarial network( CGAN). In the experiments,it adopted a generator with an U-Net structure,constrained the model with L1 term,in the adversarial training between the generator and the discriminator,model continuously learned and optimized the mapping from manga sketch to its corresponding colorful image. At last,GAN that generated model from training could be used to color manga sketch. Experiment results show to demonstrate the effectiveness of rapid colorization for manga sketch as well as the plausibility of visual effects.