Hierarchical Style Aggregation for Versatile Chinese Handwriting Generation

Few-shot Chinese handwriting generation aims to render char-acters in a target style using limited references. Existing image-levelmethods often suffer from limited stylistic fidelity and poor general-ization. We propose Hierarchical Style Aggregation for Chinese hand-writing generation (HiSAC), a versatile framework that decouples stylemodeling from task-specific generation. Our core idea is to leverage thecompositional nature of Chinese characters: by decomposing charactersinto radicals, components, and strokes, we establish fine-grained spatialcorrespondences between target content and reference glyphs at eachlevel, enabling precise style transfer. To further capture nuanced stylecharacteristics, we design a multi-band frequency encoder that extractsstylistic representations across different spectral ranges. The aggregatedmulti-granularity features can be seamlessly integrated with task-specificdecoders (e.g., Transformer for online trajectory generation, Diffusion foroffline image synthesis) without architectural redesign. By jointly model-ing style in both spatial and frequency domains, our approach enhancesrealism in global structure and stroke dynamics. Extensive experimentsdemonstrate that HiSAC outperforms existing methods both quantita-tively and qualitatively, and generalizes well to out-of-vocabulary andcross-language scripts. Code has been released at https://github.com/IIP-Lab-XDU/HiSAC.