用双注意力机制提升少样本字体生成质量，减少笔画错误和模糊。
DA-Font: Few-Shot Font Generation via Dual-Attention Hybrid Integration
- 引入双注意力模块，融合组件与关系信息指导风格迁移。
- 在多种字体上优于现有方法，显著改善结构完整性和细节保真度。
- 适合字体设计自动化、AI创意工具等场景使用。
少样本字体生成旨在仅用少量字形参考即可生成新字体，大幅降低人工设计成本。然而，由于字体风格多样且复杂，现有方法生成结果常出现笔画错误、伪影和模糊等问题。为此，我们提出DA-Font框架，集成双注意力混合模块（DAHM）。该模块包含两个协同工作的注意力块：组件注意力块利用内容图像的组件信息引导风格迁移；关系注意力块通过交互原始与风格化组件特征，进一步优化空间关系。两者协作以保持字符形状准确与风格纹理一致。此外，还设计角点一致性损失与弹性网格特征损失，强化几何对齐。大量实验表明，DA-Font在多种字体风格和字符上均超越当前最优方法，有效提升结构完整性与局部保真度。
原文摘要 · Abstract (English)
Few-shot font generation aims to create new fonts with a limited number of glyph references. It can be used to significantly reduce the labor cost of manual font design. However, due to the variety and complexity of font styles, the results generated by existing methods often suffer from visible defects, such as stroke errors, artifacts and blurriness. To address these issues, we propose DA-Font, a novel framework which integrates a Dual-Attention Hybrid Module (DAHM). Specifically, we introduce two synergistic attention blocks: the component attention block that leverages component information from content images to guide the style transfer process, and the relation attention block that further refines spatial relationships through interacting the content feature with both original and stylized component-wise representations. These two blocks collaborate to preserve accurate character shapes and stylistic textures. Moreover, we also design a corner consistency loss and an elastic mesh feature loss to better improve geometric alignment. Extensive experiments show that our DA-Font outperforms the state-of-the-art methods across diverse font styles and characters, demonstrating its effectiveness in enhancing structural integrity and local fidelity. The source code can be found at \href{https://github.com/wrchen2001/DA-Font}{\textit{https://github.com/wrchen2001/DA-Font}}.
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