Diffusion Illusions: Hiding Images in Plain Sight

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

Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors

Recent diffusion-based generative models combined with vision-language models are capable of creating realistic images from natural language prompts. While these models are trained on large internet-scale datasets, such pre-trained models are not directly introduced to any semantic localization or grounding. Most current approaches for localization or grounding rely on human-annotated localization information in the form of bounding boxes or segmentation masks. The exceptions are a few unsupervised methods that utilize architectures or loss functions geared towards localization, but they need to be trained separately. In this work, we explore how off-the-shelf diffusion models, trained with no exposure to such localization information, are capable of grounding various semantic phrases with no segmentation-specific re-training. An inference time optimization process is introduced, that is capable of generating segmentation masks conditioned on natural language. We evaluate our proposal Peekaboo for unsupervised semantic segmentation on the Pascal VOC dataset. In addition, we evaluate for referring segmentation on the RefCOCO dataset. In summary, we present a first zero-shot, open-vocabulary, unsupervised (no localization information), semantic grounding technique leveraging diffusion-based generative models with no re-training. Our code will be released publicly.

Neural Neural Textures Make Sim2Real Consistent

Unpaired image translation algorithms can be used for sim2real tasks, but many fail to generate temporally consistent results. We present a new approach that combines differentiable rendering with image translation to achieve temporal consistency over indefinite timescales, using surface consistency losses and \emph{neural neural textures}. We call this algorithm TRITON (Texture Recovering Image Translation Network): an unsupervised, end-to-end, stateless sim2real algorithm that leverages the underlying 3D geometry of input scenes by generating realistic-looking learnable neural textures. By settling on a particular texture for the objects in a scene, we ensure consistency between frames statelessly. Unlike previous algorithms, TRITON is not limited to camera movements -- it can handle the movement of objects as well, making it useful for downstream tasks such as robotic manipulation.