Generalized W-Net: Arbitrary-style Chinese Character Synthesization

TL;DR

This paper introduces the Generalized W-Net, a novel architecture that synthesizes Chinese characters in arbitrary styles with limited examples, effectively handling both seen and unseen styles and generating new content.

Contribution

The paper presents a new W-shaped architecture with Adaptive Instance Normalization for style transfer in Chinese character synthesis, capable of generalizing to unseen styles.

Findings

Effective synthesis of Chinese characters in arbitrary styles.

Handles both seen and unseen styles during training.

Generates new character contents successfully.

Abstract

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.