  Traditional methods in Chinese typography synthesis view characters as an
assembly of radicals and strokes, but they rely on manual definition of the key
points, which is still time-costing. Some recent work on computer vision
proposes a brand new approach: to treat every Chinese character as an
independent and inseparable image, so the pre-processing and post-processing of
each character can be avoided. Then with a combination of a transfer network
and a discriminating network, one typography can be well transferred to
another. Despite the quite satisfying performance of the model, the training
process requires to be supervised, which means in the training data each
character in the source domain and the target domain needs to be perfectly
paired. Sometimes the pairing is time-costing, and sometimes there is no
perfect pairing, such as the pairing between traditional Chinese and simplified
Chinese characters. In this paper, we proposed an unsupervised typography
transfer method which doesn't need pairing.
