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