GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915443447431168 |
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| author | Vu, Tai Yang, Robert |
| author_facet | Vu, Tai Yang, Robert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning Vu, Tai Yang, Robert Computer Vision and Pattern Recognition Machine Learning 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. |
| title | GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.09207 |