用深度学习将动漫草图自动转为上色图，提升制作效率。
GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
- 采用C-GAN模型实现草图到上色图的图像转换。
- 生成图像质量高、分辨率高，接近人工绘制水平。
- 适合动漫产业快速出图，降低人力成本。
从草图生成完整上色的动漫画作是漫画与动画行业中的一个大且通常成本高昂的瓶颈。本研究考察了多种用于动漫角色与其草图之间图像到图像转换的模型，包括神经风格迁移、C-GAN和CycleGAN。通过定性和定量评估，发现C-GAN是最有效的模型，能够生成接近人类创作的高质量、高分辨率图像。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。