One-shot font generation via local style self-supervision using Region-Aware Contrastive Loss

Abstract

Compositional scripts like Hangeul (Korean characters) and Chinese characters involve numerous characters, making manual font design labor-intensive and cost-ineffective work. Although many few-shot font generation methods have been introduced, they have at least one of the limitations, i.e., lacking local styles of font, additional component labeling, and high complexity in network structure and training. To solve these limitations, given our observation that font style can be perceived at a patch-level rather than a component-level, we propose Region-Aware Contrastive loss (RAC-loss) so that the generator can capture the local style by self-supervision. The proposed loss maximizes the style information between patches of the generated image and the style reference image. And we introduce an attention mechanism to the patch-level contrastive loss to handle multiple patch correspondences. This attention learns style similarity between two glyph images, which serves as a patch-correspondence map. RAC-loss gives more fine-grained feedback to the generator than component-level loss, allowing it to incorporate local styles, even in a straightforward structure like a visual geometry group network (VGGNet). This results in a fast inference latency (3.02 ms), and the proposed method achieved 43.18 mean Fréchet Inception Distance (mFID) on the test dataset, a notable decrease of 5.42 compared to the previous method.