Summary of Towards Visual Text Design Transfer Across Languages, by Yejin Choi et al.
Towards Visual Text Design Transfer Across Languages
by Yejin Choi, Jiwan Chung, Sumin Shim, Giyeong Oh, Youngjae Yu
First submitted to arxiv on: 24 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 Visual text design is crucial in conveying themes and emotions across languages, exceeding traditional translation boundaries. To evaluate the ability of visual text generation models to perform this task, we introduce MuST-Bench, a novel benchmark designed for Multimodal Style Translation (MuST). Our initial experiments reveal that existing models struggle due to inadequate textual descriptions conveying design intent. We propose SIGIL, a framework eliminating the need for style descriptions, enhancing image generation models through glyph latent for multilingual settings, pretrained VAEs for stable style guidance, and OCR with reinforcement learning feedback for readable character generation. SIGIL outperforms baselines in terms of style consistency, legibility, and visual fidelity, setting it apart from traditional description-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to translate not just words, but also the way those words look on a poster or album cover. This is called multimodal translation, and it’s important for communicating ideas across languages. We created a new test to see how well computers can do this job, which we call MuST-Bench. Currently, computers aren’t very good at this because they don’t understand the design intent behind images. To solve this problem, we developed SIGIL, a special tool that helps computers generate images while keeping the original style and meaning. |
Keywords
» Artificial intelligence » Image generation » Reinforcement learning » Text generation » Translation
