  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.
