Summary of Moyun: a Diffusion-based Model For Style-specific Chinese Calligraphy Generation, by Kaiyuan Liu et al.
Moyun: A Diffusion-Based Model for Style-Specific Chinese Calligraphy Generation
by Kaiyuan Liu, Jiahao Mei, Hengyu Zhang, Yihuai Zhang, Xingjiao Wu, Daoguo Dong, Liang He
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle a challenging task: generating Chinese calligraphy by specifying the calligrapher, font, and character style. They propose a new model called Moyun, which replaces the Unet in the Diffusion model with Vision Mamba and introduces the TripleLabel control mechanism to achieve controllable calligraphy generation. The model was tested on a large-scale dataset of over 1.9 million images, demonstrating its ability to generate calligraphy in the specified style. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Moyun model can create Chinese calligraphy by specifying the calligrapher, font, and character style. This is important because current methods only achieve style transfer but not controllable generation. The researchers used a large dataset of over 1.9 million images to test their model. |
Keywords
» Artificial intelligence » Diffusion model » Style transfer » Unet
