Impressions2Font: Generating Fonts by Specifying Impressions
This work addresses a domain-specific problem for designers and typographers by enabling font generation from textual impressions, though it appears incremental as it builds on existing GAN techniques.
The paper tackles the problem of generating font images based on specific impression words, proposing Impressions2Font (Imp2Font) as an extended conditional GAN that accepts multiple or unlearned words, with evaluations showing it generates higher-quality fonts than comparative methods.
Various fonts give us various impressions, which are often represented by words. This paper proposes Impressions2Font (Imp2Font) that generates font images with specific impressions. Imp2Font is an extended version of conditional generative adversarial networks (GANs). More precisely, Imp2Font accepts an arbitrary number of impression words as the condition to generate the font images. These impression words are converted into a soft-constraint vector by an impression embedding module built on a word embedding technique. Qualitative and quantitative evaluations prove that Imp2Font generates font images with higher quality than comparative methods by providing multiple impression words or even unlearned words.