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:摘要: 字体设计在数字内容设计和现代印刷产业中非常重要。开发能够自动合成向量字体的算法可以显著促进字体设计过程。然而,现有方法主要集中于栅格图像生成,只有少数方法能够直接合成向量字体。本文提出了一种端到端可训练的方法:VecFontSDF,通过符号距离函数(SDF)重建和合成高质量的向量字体。具体而言,基于SDF的隐式形状表示,VecFontSDF学习将每个字形建模为由几个抛物线围成的形状基元,这些基元可以精确地转换为广泛用于向量字体产品中的二次贝塞尔曲线。通过这种方式,大多数图像生成方法可以轻松扩展到合成向量字体。在公开数据集上进行的定量和定性实验表明,我们的方法在多项任务中获得高质量的结果,包括向量字体重建、插值和少样本向量字体合成,明显优于现有技术。