Font design is of vital importance in the digital content design and modern printing industry. Developing algorithms capable of automatically synthesizing vector fonts can significantly facilitate the font design process. However, existing methods mainly concentrate on raster image generation, and only a few approaches can directly synthesize vector fonts. This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts using signed distance functions (SDFs). Specifically, based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves, which can be precisely converted to quadratic B\'ezier curves that are widely used in vector font products. In this manner, most image generation methods can be easily extended to synthesize vector fonts. Qualitative and quantitative experiments conducted on a publicly-available dataset demonstrate that our method obtains high-quality results on several tasks, including vector font reconstruction, interpolation, and few-shot vector font synthesis, markedly outperforming the state of the art.

翻译：字体设计在数字内容设计与现代印刷行业中至关重要。开发能够自动合成矢量字体的算法可显著简化字体设计流程。然而，现有方法主要专注于光栅图像生成，仅有少数方法能直接合成矢量字体。本文提出一种端到端可训练的方法VecFontSDF，利用符号距离函数（SDF）重建与合成高质量矢量字体。具体而言，基于所提出的SDF隐式形状表示，VecFontSDF学习将每个字形建模为若干抛物线曲线包围的形状基元，这些曲线可精确转换为矢量字体产品中广泛使用的二次贝塞尔曲线。通过这一方式，大多数图像生成方法可轻松扩展至矢量字体合成。在公开数据集上进行的定性与定量实验表明，我们的方法在矢量字体重建、插值及少样本矢量字体合成等多个任务中均取得高质量结果，显著优于现有最先进技术。