Joint Implicit Neural Representation for High-fidelity and Compact Vector Fonts

Chia-Hao Chen, Ying-Tian Liu, Zhifei Zhang, Yuan-Chen Guo, Song-Hai Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 5538-5548

Existing vector font generation approaches either struggle to preserve high-frequency corner details of the glyph or produce vector shapes that have redundant segments, which hinders their applications in practical scenarios. In this paper, we propose to learn vector fonts from pixelated font images utilizing a joint neural representation that consists of a signed distance field (SDF) and a probabilistic corner field (CF) to capture shape corner details. To achieve smooth shape interpolation on the learned shape manifold, we establish connections between the two fields for better alignment. We further design a vectorization process to extract high-quality and compact vector fonts from our joint neural representation. Experiments demonstrate that our method can generate more visually appealing vector fonts with a higher level of compactness compared to existing alternatives.