Abstract
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{this https URL}{\textit{this https URL}}.
Abstract (translated)
少样本字体生成旨在使用有限数量的字符参考来创建新字体。这种方法可以显著降低手动设计字体的工作量。然而，由于字体风格的变化和复杂性，现有方法生成的结果往往存在明显的缺陷，如笔画错误、伪影和模糊。为了解决这些问题，我们提出了DA-Font框架，该框架集成了双注意力混合模块（DAHM）。具体来说，我们引入了两个协同的注意力块：组件注意块通过利用内容图像中的部件信息来指导风格转换过程；关系注意块进一步通过交互原始和样式化的部件级表示来细化空间关系。这两个模块协作以保持准确的文字形状和风格纹理。 此外，我们还设计了一致性角损失和弹性网格特征损失，以便更好地改善几何对齐。广泛的实验表明，在各种字体风格和字符中，我们的DA-Font方法优于最先进的方法，证明了其在增强结构完整性和局部保真度方面的有效性。源代码可以在 [此链接](this https URL) 获取。
URL
https://arxiv.org/abs/2509.16632
