Diamonds in the Sky: Pareidolic Animals in Clouds
For researchers in computer vision and human perception, this work addresses the niche problem of pareidolic animal recognition in clouds, but the approach is incremental and domain-specific.
The paper tackles the problem of predicting which animals people are likely to perceive in clouds (pareidolia) and proposes a method to help individuals perceive specific pareidolic animals. The approach uses a diffusion model to transform cloud segments into animal shapes that resemble the original cloud, achieving successful prediction and perception enhancement.
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.