Learning Stroke-Order Dynamics in Few-Shot Font Generation via Sequential Awareness

Abstract

Few-shot font generation has garnered significant attention due to its wide range of applications. The mainstream methods are based on the idea of the style and content disentangled representation learning and can be mainly categorized into two kinds of methods according to the prior used, i.e., the deep prior and glyph prior. However, the prior information used in existing methods mainly focuses on static spatial information and ignores dynamic temporal information symbolizing the internal correlation of characters, which results in stroke misalignment and poor performance on the generation of glyph articulations. To address these issues, we propose a novel few-shot font generation model by learning stroke-order dynamics via sequential awareness, where both the static spatial stroke information and dynamic temporal stroke-order information are incorporated into the generation. By leveraging these kinds of stroke information, the issues of stroke misalignment and poor articulation generation can be significantly alleviated. We conduct extensive experiments over 150 fonts, which show the superiority of the proposed model compared to state-of-the-art, and good generalization performance for the cross-lingual font generation.