@inproceedings{jiang-etal-2025-controltext,
    title = "{C}ontrol{T}ext: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations",
    author = "Jiang, Bowen  and
      Yuan, Yuan  and
      Bai, Xinyi  and
      Hao, Zhuoqun  and
      Yin, Alyson  and
      Hu, Yaojie  and
      Liao, Wenyu  and
      Ungar, Lyle  and
      Taylor, Camillo Jose",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1385/",
    doi = "10.18653/v1/2025.findings-emnlp.1385",
    pages = "25414--25425",
    ISBN = "979-8-89176-335-7",
    abstract = "This work demonstrates that diffusion models can achieve font-controllable multilingual text rendering using just raw images without font label annotations. Visual text rendering remains a significant challenge. While recent methods condition diffusion on glyphs, it is impossible to retrieve exact font annotations from large-scale, real-world datasets, which prevents user-specified font control. To address this, we propose a data-driven solution that integrates the conditional diffusion model with a text segmentation model, utilizing segmentation masks to capture and represent fonts in pixel space in a self-supervised manner, thereby eliminating the need for any ground-truth labels and enabling users to customize text rendering with any multilingual font of their choice. The experiment provides a proof of concept of our algorithm in zero-shot text and font editing across diverse fonts and languages, providing valuable insights for the community and industry toward achieving generalized visual text rendering."
}