Abstract
Artistic font generation aims to synthesize stylized glyphs based on a reference style. However, existing approaches suffer from limited style diversity and coarse control. In this work, we explore the potential of element-driven artistic font generation. Elements are the fundamental visual units of a font, serving as reference images for the desired style. Conceptually, we categorize elements into object elements (e.g., flowers or stones) with distinct structures and amorphous elements (e.g., flames or clouds) with unstructured textures. We introduce FontCrafter, an element-driven framework for font creation, and construct a large-scale dataset, ElementFont, which contains diverse element types and high-quality glyph images. However, achieving high-fidelity reconstruction of both texture and structure of reference elements remains challenging. To address this, we propose an in-context generation strategy that treats element images as visual context and uses an inpainting model to transfer element styles into glyph regions at the pixel level. To further control glyph shapes, we design a lightweight Context-aware Mask Adapter (CMA) that injects shape information. Moreover, a training-free attention redirection mechanism enables region-aware style control and suppresses stroke hallucination. In addition, edge repainting is applied to make boundaries more natural. Extensive experiments demonstrate that FontCrafter achieves strong zero-shot generation performance, particularly in preserving structural and textural fidelity, while also supporting flexible controls such as style mixture.
Abstract (translated)
艺术字体生成旨在基于参考风格合成风格化的字形。然而，现有方法存在风格多样性有限且控制粗糙的问题。本研究探索了元素驱动的艺术字体生成潜力。元素是字体的基础视觉单元，可作为期望风格的参考图像。概念上，我们将元素分为具有明确结构的实体元素（如花朵或石头）与具有非结构化纹理的无形元素（如火焰或云朵）。我们引入了FontCrafter——一个用于字体创作的元素驱动框架，并构建了包含多样化元素类型与高质量字形图像的大规模数据集ElementFont。然而，实现参考元素纹理与结构的高保真重建仍具挑战性。为此，我们提出一种上下文生成策略，将元素图像作为视觉上下文，并利用修复模型在像素层面将元素风格迁移至字形区域。为进一步控制字形形状，我们设计了轻量级的上下文感知掩模适配器（CMA）以注入形状信息。此外，一种无需训练的注意力重定向机制可实现区域感知的风格控制并抑制笔画幻觉。同时应用边缘重绘以使边界更自然。大量实验表明，FontCrafter实现了强大的零样本生成性能，尤其在保持结构与纹理保真度方面表现突出，并支持风格混合等灵活控制。
URL
https://arxiv.org/abs/2603.22054
