VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models
<|reference_start|>VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models: Artistic typography is a technique to visualize the meaning of input character in an imaginable and readable manner. With powerful text-to-image diffusion models, existing methods directly design the overall geometry and texture of input character, making it challenging to ensure both creativity and legibility. In this paper, we introduce a dual-branch and training-free method, namely VitaGlyph, enabling flexible artistic typography along with controllable geometry change to maintain the readability. The key insight of VitaGlyph is to treat input character as a scene composed of Subject and Surrounding, followed by rendering them under varying degrees of geometry transformation. The subject flexibly expresses the essential concept of input character, while the surrounding enriches relevant background without altering the shape. Specifically, we implement VitaGlyph through a three-phase framework: (i) Knowledge Acquisition leverages large language models to design text descriptions of subject and surrounding. (ii) Regional decomposition detects the part that most matches the subject description and divides input glyph image into subject and surrounding regions. (iii) Typography Stylization firstly refines the structure of subject region via Semantic Typography, and then separately renders the textures of Subject and Surrounding regions through Controllable Compositional Generation. Experimental results demonstrate that VitaGlyph not only achieves better artistry and readability, but also manages to depict multiple customize concepts, facilitating more creative and pleasing artistic typography generation. Our code will be made publicly at https://github.com/Carlofkl/VitaGlyph.<|reference_end|>
@article{feng2024vitaglyph:,
    title={VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch
  Diffusion Models},
    author={Kailai Feng, Yabo Zhang, Haodong Yu, Zhilong Ji, Jinfeng Bai, Hongzhi
  Zhang, Wangmeng Zuo},
    journal={arXiv preprint arXiv:2410.01738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01738},
    primaryClass={cs.CV cs.AI}
}