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.

翻译：对称图形是一种根据观看方向呈现不同含义的书法设计。即便是技艺精湛的艺术家，创作对称图形仍是一项极具挑战的任务，因为它要求同时兼顾两个视角下的意义表达。本研究提出通过蒸馏大规模视觉-语言扩散模型DeepFloyd IF来生成对称图形，优化字母轮廓在两种观看方向上的可读性。实验表明，我们的方法优于现有对称图形生成技术。针对英语中最常用的500个单词，该方法在单词准确率上提升超过11.6%，编辑距离至少降低41.9%。