Ambigram Generation by A Diffusion Model
This work addresses a niche problem in graphic design and computational creativity, offering a tool for both computers and human experts, but it is incremental as it applies an existing diffusion model to a new domain.
The paper tackles the problem of generating ambigrams, which are graphical letter designs readable from both original and rotated directions, by proposing a diffusion model that produces high-quality and diverse ambigrams for specified letter pairs, with quantitative and qualitative analyses confirming its effectiveness.
Ambigrams are graphical letter designs that can be read not only from the original direction but also from a rotated direction (especially with 180 degrees). Designing ambigrams is difficult even for human experts because keeping their dual readability from both directions is often difficult. This paper proposes an ambigram generation model. As its generation module, we use a diffusion model, which has recently been used to generate high-quality photographic images. By specifying a pair of letter classes, such as 'A' and 'B', the proposed model generates various ambigram images which can be read as 'A' from the original direction and 'B' from a direction rotated 180 degrees. Quantitative and qualitative analyses of experimental results show that the proposed model can generate high-quality and diverse ambigrams. In addition, we define ambigramability, an objective measure of how easy it is to generate ambigrams for each letter pair. For example, the pair of 'A' and 'V' shows a high ambigramability (that is, it is easy to generate their ambigrams), and the pair of 'D' and 'K' shows a lower ambigramability. The ambigramability gives various hints of the ambigram generation not only for computers but also for human experts. The code can be found at (https://github.com/univ-esuty/ambifusion).