DualVector: Unsupervised Vector Font Synthesis with Dual-Part
  Representation
Ying-Tian Liu Zhifei Zhang Yuan-Chen Guo Matthew Fisher Zhaowen Wang Song-Hai Zhang
Automatic generation of fonts can be an important aid to typeface design.
Many current approaches regard glyphs as pixelated images, which present
artifacts when scaling and inevitable quality losses after vectorization. On
the other hand, existing vector font synthesis methods either fail to represent
the shape concisely or require vector supervision during training. To push the
quality of vector font synthesis to the next level, we propose a novel
dual-part representation for vector glyphs, where each glyph is modeled as a
collection of closed "positive" and "negative" path pairs. The glyph contour is
then obtained by boolean operations on these paths. We first learn such a
representation only from glyph images and devise a subsequent contour
refinement step to align the contour with an image representation to further
enhance details. Our method, named DualVector, outperforms state-of-the-art
methods in vector font synthesis both quantitatively and qualitatively. Our
synthesized vector fonts can be easily converted to common digital font formats
like TrueType Font for practical use. The code is released at
https://github.com/thuliu-yt16/dualvector.