Summary of Fonts: Text Rendering with Typography and Style Controls, by Wenda Shi and Yiren Song and Dengming Zhang and Jiaming Liu and Xingxing Zou
FonTS: Text Rendering with Typography and Style Controls
by Wenda Shi, Yiren Song, Dengming Zhang, Jiaming Liu, Xingxing Zou
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper proposes a two-stage pipeline to automate visual text rendering by enhancing controllability over typography and style using diffusion transformer-based text-to-image (T2I) models. The pipeline includes parameter-efficient fine-tuning methods and enclosing typography control tokens, which enable precise word-level application of typographic features. A text-agnostic style control adapter is also proposed to prevent content leakage while enhancing style consistency. The approach is demonstrated to achieve superior word-level typographic control, font consistency, and style consistency in text rendering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way for computers to automatically make text look nice on screens or printers. It uses special models that can turn words into images, but it’s hard to get the right font and style. The researchers came up with a new approach that lets them control the font and style more precisely, even down to individual words. They also developed a way to keep the style consistent throughout the text. This makes it easier for computers to make text look good in different situations. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Parameter efficient » Transformer
