AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model
Boheng Zhao Rana Hanocka Raymond A. Yeh
Ambigrams are calligraphic designs that have different meanings depending on
the viewing orientation. Creating ambigrams is a challenging task even for
skilled artists, as it requires maintaining the meaning under two different
viewpoints at the same time. In this work, we propose to generate ambigrams by
distilling a large-scale vision and language diffusion model, namely DeepFloyd
IF, to optimize the letters' outline for legibility in the two viewing
orientations. Empirically, we demonstrate that our approach outperforms
existing ambigram generation methods. On the 500 most common words in English,
our method achieves more than an 11.6% increase in word accuracy and at least a
41.9% reduction in edit distance.