LITEGLYPH: A COMPACT AUTOREGRESSIVE FRAMEWORK FOR FEW-SHOT CHINESE VECTOR FONT GENERATION

Few-shot Chinese vector font generation requires synthesizing editable outlines for unseen characters while preserving character structure and typeface style, given a standard-form structure reference and only a few cross-character style refer- ences. Existing raster methods require post-hoc vectorization, whereas direct vector approaches often rely on specialized multi-stage pipelines or substantially larger generative models. We present LITEGLYPH, a compact unified autore- gressive framework that directly generates complete SVG command sequences. LITEGLYPH represents target-character structure as a visual prefix and encodes reference glyphs into dense style tokens that provide query-adaptive modulation throughout the decoder, allowing different SVG commands and coordinates to retrieve relevant local style evidence. We additionally introduce Raster-to-SVG initialization to establish a transferable vector prior and loop-aware rollback to improve decoding reliability. We train and evaluate on a new large-scale Chinese raster–vector corpus containing approximately 16M glyph pairs, over 2,600 cu- rated fonts, and 6,763 characters. LITEGLYPH achieves the lowest FID among the evaluated external baselines across validation, unseen-font seen-character, and unseen-font unseen-character settings, while directly producing editable SVGs with a 350M-parameter decoder. Ablations further show that dense style modulation improves unseen-font generalization over global conditioning on most metrics, and that Raster-to-SVG initialization is particularly effective when downstream font diversity is limited.