Title: Diamonds in the Sky: Pareidolic Animals in Clouds

Original: arXiv:2606.01361v1 Announce Type: new Abstract: People often see animal shapes in clouds, a phenomenon known as pareidolia. We propose an AI-based method that aims to predict which animals people are likely to perceive in clouds, even though state-of-the-art recognition methods typically fail to detect such animals. Additionally, we introduce a method to assist individuals in perceiving specific pareidolic animals, even if they did not recognize them initially. Our approach uses a diffusion model to transform cloud segments into an animal shape that visually resemble the original cloud. This diffusion technique is inspired by the observation that the diffusion process succeeds only when the target animal resembles the shape of the cloud, and that subtle visual hints often suffice to help individuals recognize specific pareidolic animals. A generated image, successfully derived from the diffusion model, is then used to predict the pareidolic animal. Additionally, a short morphing video transitioning from the generated image back to the original cloud segment is employed to further enhance the human's perception of the pareidolic animals.

Rewritten:

Title: Diamonds in the Sky: Pareidolic Animals in Clouds

Abstract: The human tendency to identify animal forms within cloud formations is a well-documented instance of pareidolia. In this work, we present an artificial intelligence framework designed to forecast the specific animals observers are prone to spot in clouds—a challenging task because conventional, state-of-the-art recognition algorithms generally struggle to identify these shapes. Furthermore, we propose a technique to aid users in recognizing particular pareidolic figures, even when those figures are not immediately apparent. Central to our methodology is a diffusion model that alters segments of clouds into animal-like structures while maintaining a strong visual continuity with the original cloud imagery. This strategy draws on the insight that diffusion processes are most effective when the intended animal shape aligns with the cloud's natural form, and that minor visual cues can be sufficient to trigger recognition in humans. Once the diffusion model generates an image, it is utilized to predict the specific pareidolic animal. To further bolster human perception, we also employ a brief morphing video that transitions from the generated animal image back to the original cloud segment.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC