Manual font design is an intricate process that transforms a stylistic visual concept into a coherent glyph set. This challenge persists in automated Few-shot Font Generation (FFG), where models often struggle to preserve both the structural integrity and stylistic fidelity from limited references. While autoregressive (AR) models have demonstrated impressive generative capabilities, their application to FFG is constrained by conventional patch-level tokenization, which neglects global dependencies crucial for coherent font synthesis. Moreover, existing FFG methods remain within the image-to-image paradigm, relying solely on visual references and overlooking the role of language in conveying stylistic intent during font design. To address these limitations, we propose GAR-Font, a novel AR framework for multimodal few-shot font generation. GAR-Font introduces a global-aware tokenizer that effectively captures both local structures and global stylistic patterns, a multimodal style encoder offering flexible style control through a lightweight language-style adapter without requiring intensive multimodal pretraining, and a post-refinement pipeline that further enhances structural fidelity and style coherence. Extensive experiments show that GAR-Font outperforms existing FFG methods, excelling in maintaining global style faithfulness and achieving higher-quality results with textual stylistic guidance.

翻译：人工字体设计是一个将风格化视觉概念转化为连贯字形集的复杂过程。这一挑战在自动化少样本字体生成（FFG）中依然存在，现有模型往往难以在有限参考样本下同时保持结构完整性与风格保真度。尽管自回归（AR）模型已展现出强大的生成能力，但其在FFG中的应用受限于传统的局部块级标记化方法，该方法忽略了字体合成中至关重要的全局依赖关系。此外，现有FFG方法仍局限于图像到图像的范式，仅依赖视觉参考而忽视了语言在字体设计过程中传达风格意图的作用。为应对这些局限，我们提出GAR-Font——一种用于多模态少样本字体生成的新型自回归框架。GAR-Font引入了全局感知标记器以有效捕捉局部结构与全局风格模式，通过轻量级语言-风格适配器实现灵活风格控制的多模态风格编码器（无需密集的多模态预训练），以及进一步提升结构保真度与风格一致性的后优化流程。大量实验表明，GAR-Font在保持全局风格忠实度方面优于现有FFG方法，并能通过文本风格指导获得更高质量的生成结果。