LoGAN: Multilingual Font Localization with Generative Agents

Localizing a font into new languages is a highly intricate task requiring precisedesign adaptation of glyphs, color/texture, and spacing/kerning, from source to target lan-guages. Most existing methods focus on single glyph generation with limited capability inhandling multilingual font rendering. In this work, we propose LoGAN, a VLM-based agenticframework for few-shot multilingual font localization, which takes in a small number of indi-vidual glyphs from a font or letters from a logo and uses them to generate complete charactersets in other languages. LoGAN breaks down this task into multiple components: a glyph-leveldiffusion model, a style finetuning module, a spacing and kerning transfer algorithm, and atexture expansion model, with a VLM agent coordinator. LoGAN achieves broad languagecoverage 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 than27 languages and compare it against both specialized font generation and state-of-the-artimage editing models with strong text rendering capabilities (e.g., FLUX, Nano-Banana).Our approach yields higher glyph fidelity while maintaining better style, texture, and kerningconsistency according to both quantitative and qualitative evaluations.