AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model

December 05, 2023 · Entered Twilight · 🏛 arXiv.org

"No code URL or promise found in abstract"
"Code repo scraped from project page (backfill)"

Evidence collected by the PWNC Scanner

Repo contents: LICENSE, README.md, _git, ambigram, ambigram_sample_random.py, ambigram_train.py, ambigramability, calc_ambigramability.py, configs, demo.py, evaluation_script, guided_diffusion, ldm, pre-train, requirements.txt

Authors Boheng Zhao, Rana Hanocka, Raymond A. Yeh arXiv ID 2312.02967 Category cs.CV: Computer Vision Citations 2 Venue arXiv.org Repository https://github.com/univ-esuty/ambifusion ⭐ 16 Last Checked 2 months ago

Abstract

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.