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Automatic font generation based on deep learning has aroused a lot ofinterest in the last decade. However, only a few recently-reported approachesare capable of directly generating vector glyphs and their results are stillfar from satisfactory. In this paper, we propose a novel method, DeepVecFont,to effectively resolve this problem. Using our method, for the first time,visually-pleasing vector glyphs whose quality and compactness are bothcomparable to human-designed ones can be automatically generated. The key ideaof our DeepVecFont is to adopt the techniques of image synthesis, sequencemodeling and differentiable rasterization to exhaustively exploit thedual-modality information (i.e., raster images and vector outlines) of vectorfonts. The highlights of this paper are threefold. First, we design adual-modality learning strategy which utilizes both image-aspect andsequence-aspect features of fonts to synthesize vector glyphs. Second, weprovide a new generative paradigm to handle unstructured data (e.g., vectorglyphs) by randomly sampling plausible synthesis results to get the optimal onewhich is further refined under the guidance of generated structured data (e.g.,glyph images). Finally, qualitative and quantitative experiments conducted on apublicly-available dataset demonstrate that our method obtains high-qualitysynthesis results in the applications of vector font generation andinterpolation, significantly outperforming the state of the art.

DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning