  Synthesizing Chinese characters with consistent style using few stylized
examples is challenging. Existing models struggle to generate arbitrary style
characters with limited examples. In this paper, we propose the Generalized
W-Net, a novel class of W-shaped architectures that addresses this. By
incorporating Adaptive Instance Normalization and introducing multi-content,
our approach can synthesize Chinese characters in any desired style, even with
limited examples. It handles seen and unseen styles during training and can
generate new character contents. Experimental results demonstrate the
effectiveness of our approach.
