mlsanityData to 7 Oct 2026

Moyun: A Diffusion-Based Model for Style-Specific Chinese Calligraphy Generation

arXiv 2410.07618 ↗First posted 10 Oct 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

Although Chinese calligraphy generation has achieved style transfer, generating calligraphy by specifying the calligrapher, font, and character style remains challenging. To address this, we propose a new Chinese calligraphy generation model '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 our large-scale dataset 'Mobao' of over 1.9 million images, and the results demonstrate that 'Moyun' can effectively control the generation process and produce calligraphy in the specified style. Even for calligraphy the calligrapher has not written, 'Moyun' can generate calligraphy that matches the style of the calligrapher.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 10 Oct 2024.

Builds on

19 of 28 references.

References · 19

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  9. 2022Scalable Diffusion Models with Transformerscited by 5,931
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  11. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  12. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
7 more
  1. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  2. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,283
  3. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  4. 2020CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generatorcited by 28
  5. 2021ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approachcited by 20
  6. 2023Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modelingcited by 9
  7. 2024CalliffusionV2: Personalized Natural Calligraphy Generation with Flexible Multi-modal Controlcited by 3

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025UniCalli: A Unified Diffusion Framework for Column-Level Generation and Recognition of Chinese Calligraphycited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026InkDiffuser: High-Fidelity One-shot Chinese Calligraphy via Differentiable Morphological Optimization
  2. 2023CalliPaint: Chinese Calligraphy Inpainting with Diffusion Model
  3. 2026CalliMaster: Mastering Page-level Chinese Calligraphy via Layout-guided Spatial Planning
  4. 2024Few-shot Calligraphy Style Learning
  5. 2024DiffCJK: Conditional Diffusion Model for High-Quality and Wide-coverage CJK Character Generation
  6. 2025Layout Stroke Imitation: A Layout Guided Handwriting Stroke Generation for Style Imitation with Diffusion Model
  7. 2025Text-Conditioned Diffusion Model for High-Fidelity Korean Font Generation
  8. 2026Structure-Level Disentangled Diffusion for Few-Shot Chinese Font Generation