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.
翻译:自动生成字体仍是一个具有挑战性的研究问题,原因是字符数量庞大,结构复杂。 通常,只有少数样本可以用作风格/内容参考( 以几发方式学习), 这进一步增加了保存本地风格模式或详细光学结构的难度。 我们调查了以往研究的缺点,发现粗糙的区分器不足以监督字体生成器。 为此,我们提议了一个新型的组件软件模块( CAM ), 监督生成器在更精细的层次( 即 组件级别) 上分解内容和风格。 不同于以往努力提高生成器复杂性的研究, 我们的目标是对一个相对简单的生成器进行更有效的监督, 以充分发挥其潜力, 这对于生成字体来说是一个全新的视角。 整个框架通过将组件级别监督与对抗性学习相结合而取得显著的成果。 因此我们称之为组件- Guided GAN, 很快的 CG- GAN。 广泛的实验显示, 我们的方法超越了最先进的一发型字体生成方法。 此外, 它可以应用到手写式的文字合成和一般图像生成方法。