Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator

• URL: http://arxiv.org/abs/2205.00146v1
• Date: Sat, 30 Apr 2022 03:41:49 GMT
• Title: Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator
• Authors: Yuxin Kong, Canjie Luo, Weihong Ma, Qiyuan Zhu, Shenggao Zhu, Nicholas Yuan, Lianwen Jin
• Abstract summary: We propose a novel Component-Aware Module (CAM), which supervises the generator to decouple content and style at a more fine-grained level. Our approach outperforms state-of-the-art one-shot font generation methods. It can be applied to handwritten word synthesis and scene text image editing.
• Score: 28.325133809296464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures. Typically, only a few samples can serve as the style/content reference (termed few-shot learning), which further increases the difficulty to preserve local style patterns or detailed glyph structures. We investigate the drawbacks of previous studies and find that a coarse-grained discriminator is insufficient for supervising a font generator. To this end, we propose a novel Component-Aware Module (CAM), which supervises the generator to decouple content and style at a more fine-grained level, \textit{i.e.}, the component level. Different from previous studies struggling to increase the complexity of generators, we aim to perform more effective supervision for a relatively simple generator to achieve its full potential, which is a brand new perspective for font generation. The whole framework achieves remarkable results by coupling component-level supervision with adversarial learning, hence we call it Component-Guided GAN, shortly CG-GAN. Extensive experiments show that our approach outperforms state-of-the-art one-shot font generation methods. Furthermore, it can be applied to handwritten word synthesis and scene text image editing, suggesting the generalization of our approach.

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