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：手动字体设计是一个将风格化视觉概念转化为一致字形集的复杂过程。这一挑战在自动化少样本字体生成（FFG）中依然存在，模型常难以在有限参考样本中同时保持结构完整性与风格保真度。尽管自回归（AR）模型展现了强大的生成能力，但其在FFG中的应用受限于传统的图像块级分词化处理——这种处理忽视了对于连贯字体合成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略了语言在字体设计过程中传达风格意图的作用。为解决这些局限，我们提出GAR-Font——一个面向多模态少样本字体生成的新型自回归框架。GAR-Font引入了一种能够有效捕获局部结构与全局风格模式的全域感知分词器、一个通过轻量级语言风格适配器实现灵活风格控制且无需大规模多模态预训练的多模态风格编码器，以及一个可进一步提升结构保真度与风格一致性的后处理优化管线。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，并能借助文本风格指导获得更高质量的生成结果。