Abstract
The process of generating fully colorized drawings from sketches is a large, usually costly bottleneck in the manga and anime industry. In this study, we examine multiple models for image-to-image translation between anime characters and their sketches, including Neural Style Transfer, C-GAN, and CycleGAN. By assessing them qualitatively and quantitatively, we find that C-GAN is the most effective model that is able to produce high-quality and high-resolution images close to those created by humans.
Abstract (translated)
从草图生成全彩绘制的过程在漫画和动画行业中是一个庞大且通常成本高昂的瓶颈。在这项研究中，我们考察了多种用于在动漫角色与其草图之间进行图像到图像转换的模型，包括神经风格迁移（Neural Style Transfer）、条件生成对抗网络（C-GAN）和循环GAN（CycleGAN）。通过定性和定量评估，我们发现C-GAN是最有效的模型，能够产生接近人类创作的高度质量和高分辨率的图像。
URL
https://arxiv.org/abs/2508.09207
