  Due to the huge category number, the sophisticated combinations of various
strokes and radicals, and the free writing or printing styles, generating
Chinese characters with diverse styles is always considered as a difficult
task. In this paper, an efficient and generalized deep framework, namely, the
W-Net, is introduced for the one-shot arbitrary-style Chinese character
generation task. Specifically, given a single character (one-shot) with a
specific style (e.g., a printed font or hand-writing style), the proposed W-Net
model is capable of learning and generating any arbitrary characters sharing
the style similar to the given single character. Such appealing property was
rarely seen in the literature. We have compared the proposed W-Net framework to
many other competitive methods. Experimental results showed the proposed method
is significantly superior in the one-shot setting.
