  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
