Abstract
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 Bézier 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.
Abstract (translated)
字体设计在数字内容设计和现代印刷业中至关重要。开发能够自动合成矢量字体的算法可以大大简化字体设计过程。然而,现有的方法主要关注Raster image generation,而且只有几种方法可以直接合成矢量字体。本文提出了一种端到端训练的方法, VecFontSDF,使用 signed distance functions (SDFs) 重构和合成高质量的矢量字体。具体来说,基于所提出的SDF为基础的隐含形状表示, VecFontSDF学习将每个字符视为形状基本单元,被精确转换为在矢量字体产品中广泛使用的 quadratic Bezier 曲线。因此,大多数生成方法可以轻松地扩展用于合成矢量字体。在公开数据集上进行定性和定量实验表明,我们的方法在多个任务中取得了高质量的结果,包括矢量字体重建、插值和少量的矢量字体合成,显著超越了现有技术水平。
URL
https://arxiv.org/abs/2303.12675
