# Diamonds in the Sky: Pareidolic Animals in Clouds

arXiv: https://arxiv.org/abs/2606.01361 · HTML: https://mlsanity.com/paper/2606.01361/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 31 May 2026.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [OneFormer: One Transformer to Rule Universal Image Segmentation](https://mlsanity.com/paper/2211.06220/) (2022)
- [Simple Open-Vocabulary Object Detection with Vision Transformers](https://mlsanity.com/paper/2205.06230/) (2022)
- [Delta Denoising Score](https://mlsanity.com/paper/2304.07090/) (2023)
- [Everything's Talkin': Pareidolia Face Reenactment](https://mlsanity.com/paper/2104.03061/) (2021)
- [Seeing Faces in Things: A Model and Dataset for Pareidolia](https://mlsanity.com/paper/2409.16143/) (2024)

## Abstract (arXiv)

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.