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Along the rapid development of deep learning techniques in generative models,it is becoming an urgent issue to combine machine intelligence with humanintelligence to solve the practical applications. Motivated by thismethodology, this work aims to adjust the machine generated character fontswith the effort of human workers in the perception study. Although numerousfonts are available online for public usage, it is difficult and challenging togenerate and explore a font to meet the preferences for common users. To solvethe specific issue, we propose the perceptual manifold of fonts to visualizethe perceptual adjustment in the latent space of a generative model of fonts.In our framework, we adopt the variational autoencoder network for the fontgeneration. Then, we conduct a perceptual study on the generated fonts from themulti-dimensional latent space of the generative model. After we obtained thedistribution data of specific preferences, we utilize manifold learningapproach to visualize the font distribution. In contrast to the conventionaluser interface in our user study, the proposed font-exploring user interface isefficient and helpful in the designated user preference.

Learning Perceptual Manifold of Fonts