Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator
This work addresses the problem of generating fonts with limited reference samples for designers and AI applications, offering a novel supervision approach that is incremental in improving existing methods.
The paper tackles the challenge of one-shot font generation by proposing a Component-Guided GAN (CG-GAN) that uses a component-based discriminator for fine-grained supervision, achieving state-of-the-art results in font generation and demonstrating generalization to handwritten word synthesis and scene text editing.
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, 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.