UC-Font: Unified Content Representation for Few-Shot Font Generation

Few-shot font generation (FFG) aims to synthesize stylistically consistent characters from limited reference glyphs. The font content sources are restricted to either standard-font glyph images or component sequences, such as radical sequences and stroke sequences, failing to support image and text modality inputs within a unified paradigm. In this paper, we propose UC-Font, a unified framework for FFG with versatile input compatibility. It contains a Unified-Content Representer (UCR) to unify character content representation through cross-modal alignment at local and global levels, enabling flexible input of both glyph images and structural component sequences. For style aggregation, conventional cross-attention fails to adequately suppress irrelevant features, causing stylistic inconsistencies between generated fonts and target fonts even for identical character components. To tackle this issue, we present a Temperature-Adjusted Style Aggregator (TASA), which leverages component similarity-conditioned attention to strengthen the style consistency of shared character components. UC-Font could be built upon diffusion-based and flow matching-based frameworks, where FFG is formulated as a conditional generation task with fused unified contents and aggregated style features serving as conditions. To enhance the content and style feature interaction through joint attention modeling for FFG, a Dual-Projection Fusion (DPF) module is adopted. Extensive experiments show that UC-Font outperforms state-of-the-art FFG methods.