XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation

TL;DR

This paper introduces XMP-Font, a self-supervised cross-modality pre-training approach with a transformer encoder for few-shot font generation, effectively capturing complex style features without fine-tuning.

Contribution

It proposes a novel self-supervised pre-training strategy and a cross-modality transformer encoder to improve style representation in few-shot font generation.

Findings

Successfully transfers styles at all scales.

Requires only one reference glyph.

Achieves 28% fewer bad cases than state-of-the-art methods.

Abstract

Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Few-shot font generation is thus required, as it requires only a few glyph references without fine-tuning during test. Existing methods follow the style-content disentanglement paradigm and expect novel fonts to be produced by combining the style codes of the reference glyphs and the content representations of the source. However, these few-shot font generation methods either fail to capture content-independent style representations, or employ localized component-wise style representations, which is insufficient to model many Chinese font styles that involve hyper-component features such as inter-component spacing and "connected-stroke". To resolve these drawbacks and make the style representations more reliable, we propose a self-supervised cross-modality pre-training strategy and a cross-modality transformer-based encoder that is conditioned jointly on the glyph image and the corresponding stroke labels. The cross-modality encoder is pre-trained in a self-supervised manner to allow effective capture of cross- and intra-modality correlations, which facilitates the content-style disentanglement and modeling style representations of all scales (stroke-level, component-level and character-level). The pre-trained encoder is then applied to the downstream font generation task without fine-tuning. Experimental comparisons of our method with state-of-the-art methods demonstrate our method successfully transfers styles of all scales. In addition, it only requires one reference glyph and achieves the lowest rate of bad cases in the few-shot font generation task 28% lower than the second best