HQ-Font: Few-shot Font Generation via Transferring Hierarchical Quantization Styles

Abstract Utilizing artificial intelligence for few-shot font generation (FFG) has become a trend in designing fonts for glyph-rich scripts. Most existing FFG approaches either globally disentangle the content and style of reference glyphs or decompose glyphs into strokes or radicals, then transfer these styles component-wise. However, they may fail to distinguish fine-grained local details or require predefined decomposition rules or special infeasible training strategies. This paper introduces a Hierarchical Quantization-based FFG approach (HQ-Font) by aggregating different-grained styles. It adopts a vector quantization strategy for glyph representation through unsupervised learning, enabling the contrasting and learning of discrete latent representations from low to high-level glyph feature spaces simultaneously without manual definition. A cross-attention mechanism is employed to transfer different granular styles of reference glyphs onto the discrete latent codes through contrastive learning, generating a complete set of content-agnostic style representations for different scripts. To this end, a font generation decoder performs hierarchical font synthesis, gradually mapping the corresponding global semantic and local stroke stylized codes from a given input to the final font image output. The number of glyph references as input can vary without the need for fine-tuning during testing, making HQ-Font more flexible. The experimental results demonstrate the effectiveness and generalizability of HQ-Font across different linguistic scripts and also show its superiority when compared with other state-of-the-art FFG methods.