  Automatic font generation without human experts is a practical and
significant problem, especially for some languages that consist of a large
number of characters. Existing methods for font generation are often in
supervised learning. They require a large number of paired data, which are
labor-intensive and expensive to collect. In contrast, common unsupervised
image-to-image translation methods are not applicable to font generation, as
they often define style as the set of textures and colors. In this work, we
propose a robust deformable generative network for unsupervised font generation
(abbreviated as DGFont++). We introduce a feature deformation skip connection
(FDSC) to learn local patterns and geometric transformations between fonts. The
FDSC predicts pairs of displacement maps and employs the predicted maps to
apply deformable convolution to the low-level content feature maps. The outputs
of FDSC are fed into a mixer to generate final results. Moreover, we introduce
contrastive self-supervised learning to learn a robust style representation for
fonts by understanding the similarity and dissimilarities of fonts. To
distinguish different styles, we train our model with a multi-task
discriminator, which ensures that each style can be discriminated
independently. In addition to adversarial loss, another two reconstruction
losses are adopted to constrain the domain-invariant characteristics between
generated images and content images. Taking advantage of FDSC and the adopted
loss functions, our model is able to maintain spatial information and generates
high-quality character images in an unsupervised manner. Experiments
demonstrate that our model is able to generate character images of higher
quality than state-of-the-art methods.
