  The mood of a text and the intention of the writer can be reflected in the
typeface. However, in designing a typeface, it is difficult to keep the style
of various characters consistent, especially for languages with lots of
morphological variations such as Chinese. In this paper, we propose a Typeface
Completion Network (TCN) which takes one character as an input, and
automatically completes the entire set of characters in the same style as the
input characters. Unlike existing models proposed for image-to-image
translation, TCN embeds a character image into two separate vectors
representing typeface and content. Combined with a reconstruction loss from the
latent space, and with other various losses, TCN overcomes the inherent
difficulty in designing a typeface. Also, compared to previous image-to-image
translation models, TCN generates high quality character images of the same
typeface with a much smaller number of model parameters. We validate our
proposed model on the Chinese and English character datasets, which is paired
data, and the CelebA dataset, which is unpaired data. In these datasets, TCN
outperforms recently proposed state-of-the-art models for image-to-image
translation. The source code of our model is available at
https://github.com/yongqyu/TCN.
