AI RESEARCH
Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation
arXiv CS.CV
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ArXi:2601.01593v2 Announce Type: replace Manual font design is an intricate process that transforms a stylistic visual concept into a coherent glyph set. This challenge persists in automated Few-shot Font Generation (FFG), where models often struggle to preserve both the structural integrity and stylistic fidelity from limited references. While autoregressive (AR) models have nstrated impressive generative capabilities, their application to FFG is constrained by conventional patch-level tokenization, which neglects global dependencies crucial for coherent font synthesis.