Guo, YuanLian, ZhouhuiTang, YingminXiao, JianguoDiamanti, Olga and Vaxman, Amir2018-04-142018-04-1420181017-4656https://doi.org/10.2312/egs.20181045https://diglib.eg.org:443/handle/10.2312/egs20181045The design of fonts, especially Chinese fonts, is known as a tough task that requires considerable time and professional skills. In this paper, we propose a method to easily generate Chinese font libraries in new styles based on manifold learning and adversarial networks. Starting from a number of existing fonts that cover various styles, we firstly use convolutional neural networks to obtain the representation features of these fonts, and then build a font manifold via non-linear mapping. Using the font manifold, we can interpolate and move between those existing fonts to get new font features, which are then fed into a generative network learned via adversarial training to generate the whole new font libraries. Experimental results demonstrate that high-quality Chinese fonts in various new styles against existing ones can be efficiently generated using our method.I.3.3 [Computer Graphics]Picture/Image GenerationLine and curve generationI.2.4 [Artificial Intelligence]LearningConnectionism and neural nets10.2312/egs.2018104561-64