Problem
Research questions and friction points this paper is trying to address.
Automatically synthesizing high-quality vector fonts
Extending image generation methods to vector fonts
Improving vector font reconstruction and interpolation
VecFontSDF: Learning to Reconstruct and Synthesize High-Quality Vector Fonts via Signed Distance Functions
🤖 AI Summary
Existing font generation methods are constrained to raster images and struggle to synthesize editable vector fonts directly. To address this, we propose the first end-to-end trainable framework for vector font reconstruction and synthesis. Our core innovation is the introduction of Signed Distance Functions (SDFs) into vector font modeling: glyphs are implicitly represented using geometric primitives bounded by parabolas, and these SDFs are analytically converted into quadratic Bézier curves. The framework enables high-fidelity reconstruction, cross-font interpolation, and few-shot synthesis, facilitating a natural transition from image-based input to vector font output. Evaluated on public benchmarks, our method surpasses state-of-the-art approaches in fidelity, editability, and compactness. It establishes a new paradigm for automated, vector-native font design.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses signed distance functions for vector fonts
Converts glyphs to quadratic Bézier curves
Extends image methods to vector font synthesis
🔎 Similar Papers
No similar papers found.
