用生成式智能体实现少样本多语言字体迁移，支持中日韩等复杂文字。
LoGAN: Multilingual Font Localization with Generative Agents
- 基于视觉语言模型的智能体框架，分步处理字形、风格、间距与纹理迁移。
- 在27种语言上实现高保真字形生成，风格与字距一致性优于主流图像编辑模型。
- 适合需要跨语言字体设计的工业应用，尤其擅长中日韩等复杂文字系统。
将字体适配至新语言是一项复杂任务，需精准调整字形、颜色/纹理及间距/字距，现有方法多仅限单字形生成且难以支持多语言渲染。本文提出LoGAN，一种基于视觉语言模型的生成式智能体框架，可在少量样本下完成多语言字体本地化，输入少量原始字形或标志字母即可生成目标语言完整字符集。该框架包含字形级扩散模型、风格微调模块、间距与字距迁移算法及纹理扩展模型，并由视觉语言模型智能体统一协调。在涵盖超过27种语言的字体与真实标志数据集上评估，相比专门的字体生成方法及先进图像编辑模型（如FLUX、Nano-Banana），LoGAN在定量与定性评价中均展现出更高字形保真度，同时保持更优的风格、纹理与字距一致性，适用于包括中文、日文、韩文在内的多种语言系统。
原文摘要 · Abstract (English)
Localizing a font into new languages is a highly intricate task requiring precise design adaptation of glyphs, color/texture, and spacing/kerning, from source to target languages. Most existing methods focus on single glyph generation with limited capability in handling multilingual font rendering. In this work, we propose LoGAN, a VLM-based agentic framework for few-shot multilingual font localization, which takes in a small number of individual glyphs from a font or letters from a logo and uses them to generate complete character sets in other languages. LoGAN breaks down this task into multiple components: a glyph-level diffusion model, a style finetuning module, a spacing and kerning transfer algorithm, and a texture expansion model, with a VLM agent coordinator. LoGAN achieves broad language coverage for font localization with various styles, including Chinese/Korean/Japanese (CJK). We evaluate our approach on both font and real-world logo datasets spanning more than 27 languages and compare it against both specialized font generation and state-of-the-art image editing models with strong text rendering capabilities (e.g., FLUX, Nano-Banana). Our approach yields higher glyph fidelity while maintaining better style, texture, and kerning consistency according to both quantitative and qualitative evaluations.
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