发表机构
Netflix（网飞公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出LoGAN，一个基于VLM的生成式智能体框架，通过字形扩散、风格微调、间距迁移和纹理扩展等模块，实现少样本多语言字体本地化，在27种以上语言上取得更高字形保真度与一致性。
AI 中文摘要
将字体本地化为新语言是一项高度复杂的任务，需要从源语言到目标语言对字形、颜色/纹理以及间距/字距进行精确的设计调整。现有大多数方法专注于单个字形生成，在处理多语言字体渲染方面能力有限。在这项工作中，我们提出了LoGAN，一个基于视觉语言模型（VLM）的智能体框架，用于少样本多语言字体本地化。该框架接收来自一种字体的少量单个字形或来自标志的字母，并利用它们生成其他语言的完整字符集。LoGAN将这一任务分解为多个组成部分：一个字形级扩散模型、一个风格微调模块、一个间距和字距迁移算法以及一个纹理扩展模型，并由一个VLM智能体协调器进行统一管理。LoGAN实现了对多种风格字体本地化的广泛语言覆盖，包括中文/韩文/日文（CJK）。我们在涵盖超过27种语言的字体和真实世界标志数据集上评估了我们的方法，并将其与专门的字体生成模型以及具有强大文本渲染能力的最先进图像编辑模型（如FLUX、Nano-Banana）进行了比较。根据定量和定性评估，我们的方法在保持更好的风格、纹理和字距一致性的同时，实现了更高的字形保真度。
英文摘要
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.
CommentsAccepted by ECCV2026
