GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 10 Aug 2025.
Builds on
10 of 17 references.
References · 10
- 2022Generative Adversarial Networkscited by 5,627
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2016Colorful Image Colorizationcited by 1,114
- 2015A Neural Algorithm of Artistic Stylecited by 483
- 2017Exploring the structure of a real-time, arbitrary neural artistic stylization networkcited by 89
- 2020How Not to Give a FLOP: Combining Regularization and Pruning for Efficient Inferencecited by 3
- 2017Outline Colorization through Tandem Adversarial Networkscited by 2
- 2020FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniquescited by 2
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Citing papers per year: 2025: 1.
1 paper.