  In this work, we propose a novel framework named Coconditional Autoencoding
Adversarial Networks (CocoAAN) for Chinese font learning, which jointly learns
a generation network and two encoding networks of different feature domains
using an adversarial process. The encoding networks map the glyph images into
style and content features respectively via the pairwise substitution
optimization strategy, and the generation network maps these two kinds of
features to glyph samples. Together with a discriminative network conditioned
on the extracted features, our framework succeeds in producing
realistic-looking Chinese glyph images flexibly. Unlike previous models relying
on the complex segmentation of Chinese components or strokes, our model can
"parse" structures in an unsupervised way, through which the content feature
representation of each character is captured. Experiments demonstrate our
framework has a powerful generalization capacity to other unseen fonts and
characters.
