  Font generation is a challenging problem especially for some writing systems
that consist of a large number of characters and has attracted a lot of
attention in recent years. However, existing methods for font generation are
often in supervised learning. They require a large number of paired data, which
is labor-intensive and expensive to collect. Besides, common image-to-image
translation models often define style as the set of textures and colors, which
cannot be directly applied to font generation. To address these problems, we
propose novel deformable generative networks for unsupervised font generation
(DGFont). We introduce a feature deformation skip connection (FDSC) which
predicts pairs of displacement maps and employs the predicted maps to apply
deformable convolution to the low-level feature maps from the content encoder.
The outputs of FDSC are fed into a mixer to generate the final results. Taking
advantage of FDSC, the mixer outputs a high-quality character with a complete
structure. To further improve the quality of generated images, we use three
deformable convolution layers in the content encoder to learn style-invariant
feature representations. Experiments demonstrate that our model generates
characters in higher quality than state-of-art methods. The source code is
available at https://github.com/ecnuycxie/DG-Font.
