Diffusion Illusions: Hiding Images in Plain Sight
This work addresses the challenge of computationally creating optical illusions for applications in art, design, and entertainment, representing a novel domain-specific advancement.
The authors tackled the problem of automatically generating 'prime' images that create optical illusions when physically arranged, by introducing Diffusion Illusions, a pipeline that uses a frozen text-to-image diffusion model with adapted and new losses to produce illusions aligning with user prompts, and they demonstrated successful physical fabrication.
We explore the problem of computationally generating special `prime' images that produce optical illusions when physically arranged and viewed in a certain way. First, we propose a formal definition for this problem. Next, we introduce Diffusion Illusions, the first comprehensive pipeline designed to automatically generate a wide range of these illusions. Specifically, we both adapt the existing `score distillation loss' and propose a new `dream target loss' to optimize a group of differentially parametrized prime images, using a frozen text-to-image diffusion model. We study three types of illusions, each where the prime images are arranged in different ways and optimized using the aforementioned losses such that images derived from them align with user-chosen text prompts or images. We conduct comprehensive experiments on these illusions and verify the effectiveness of our proposed method qualitatively and quantitatively. Additionally, we showcase the successful physical fabrication of our illusions -- as they are all designed to work in the real world. Our code and examples are publicly available at our interactive project website: https://diffusionillusions.com