CLIP-FONT: SEMENTIC SELF-SUPERVISED FEW-SHOT FONT GENERATION WITH CLIP

Font design is a very resource-intensive endeavor, especially for intricate fonts. The task of few-shot font generation (FFG) has attracted great interest recently. This method captures style from a limited set of reference glyphs and then transfers it to other characters to generate diverse style fonts. Existing FFG methods mainly revolve around learning font content or style. However, these methods often only learn content or style or lack the ability to represent style and content, resulting in poor font quality. To address these issues, we introduce CLIP-Font-a novel few-shot font generation model. CLIP-Font uses font text semantics for self-supervision to guide font generation at the content level, and uses attention-based contrast learning at the style level to capture the representation capabilities of the font fine-grained style enhancement model. Experimental results on various datasets demonstrate the effectiveness of our method, surpassing the performance of existing FFG techniques.