VecFontSDF：通过有符号距离函数学习重建与合成高质量矢量字体
VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functions
AI总结：
本文提出VecFontSDF，利用有符号距离函数将字形建模为可精确转为二次贝塞尔曲线的抛物线基元，实现高质量矢量字体重建、插值与少样本合成，显著优于现有方法。
摘要：
字体设计在数字内容设计和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矢量字体。本文提出了一种端到端可训练的方法VecFontSDF，利用有符号距离函数（SDF）重建和合成高质量矢量字体。具体而言，基于所提出的基于SDF的隐式形状表示，VecFontSDF学习将每个字形建模为由若干抛物线包围的形状基元，这些基元可以精确转换为矢量字体产品中广泛使用的二次贝塞尔曲线。通过这种方式，大多数图像生成方法可以很容易地扩展用于合成矢量字体。在公开数据集上进行的定性和定量实验表明，我们的方法在多个任务上取得了高质量的结果，包括矢量字体重建、插值和少样本矢量字体合成，显著优于现有最先进方法。我们的代码和训练模型可在https://xiazeqing.github.io/VecFontSDF获取。
英文摘要：
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. Our code and trained models are available at https://xiazeqing.github.io/VecFontSDF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
