超越图块：面向多模态少样本字体生成的全局感知自回归模型
Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation
全局感知分词器与轻量语言-风格适配器可迁移到其他需要全局一致性的自回归图像生成任务，如场景文字生成或风格化草图合成。
原文依据 GAR-Font introduces a global-aware tokenizer that effectively captures both local structures and global stylistic patterns
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论手动字体设计是一个复杂的过程，需要将风格化的视觉概念转化为连贯的字形集合。这一挑战在自动化少样本字体生成（FFG）中依然存在，模型难以从有限的参考中同时保持结构完整性和风格保真度。尽管自回归（AR）模型已展现出令人印象深刻的生成能力，但其在FFG中的应用受限于传统的图块级分词，忽略了连贯字体合成所必需的全局依赖关系。此外，现有的FFG方法仍停留在图像到图像的范式内，仅依赖视觉参考，忽视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，我们提出了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引入了一个全局感知分词器，能够有效捕捉局部结构和全局风格模式；一个多模态风格编码器，通过轻量级语言-风格适配器提供灵活的风格控制，且无需大量多模态预训练；以及一个后细化流程，进一步提升结构保真度和风格连贯性。大量实验表明，GAR-Font优于现有的FFG方法，在保持全局风格忠实度方面表现突出，并在文本风格引导下获得更高质量的结果。项目页面：https://xtryer-s.github.io/projects_pages/GAR_Font。
英文原文摘要 1476 字符
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 struggle to preserve both 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. Project Page: https://xtryer-s.github.io/projects_pages/GAR_Font.