Authors

Pascal Chang (DisneyResearch|Studios/ETH Joint PhD)

Sergio Sancho (DisneyResearch|Studios/ETH Joint PhD)

Jingwei Tang (DisneyResearch|Studios)

Markus Gross (DisneyResearch|Studios)

Vinicius Azevedo (DisneyResearch|Studios)

Abstract

Anamorphosis refers to a category of images that are intentionally distorted, making them unrecognizable when viewed directly. Their true form only reveals itself when seen from a specific viewpoint, which can be through some catadioptric device like a mirror or a lens. While the construction of these mathematical devices can be traced back to as early as the 17th century, they are only interpretable when viewed from a specific vantage point and tend to lose meaning when seen normally. In this paper, we revisit these famous optical illusions with a generative twist. With the help of latent rectified flow models, we propose a method to create anamorphic images that still retain a valid interpretation when viewed directly. To this end, we introduce Laplacian Pyramid Warping, a frequency-aware image warping technique key to generating high-quality visuals. Our work extends Visual Anagrams to latent space models and to a wider range of spatial transforms, enabling the creation of novel generative perceptual illusions.

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