mlsanityData to 13 Oct 2026

Diamonds in the Sky: Pareidolic Animals in Clouds

arXiv 2606.01361 ↗First posted 31 May 2026cs.CVcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

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.

Key facts. No citing papers in the map yet (data through 13 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.

Builds on

13 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022DreamFusion: Text-to-3D using 2D Diffusion29 Sep

Other references · 12

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,550
  3. 2023Segment Anythingcited by 8,195
  4. 2020End-to-End Object Detection with Transformerscited by 7,396
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,564
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,339
  7. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,344
  8. 2022OneFormer: One Transformer to Rule Universal Image Segmentationcited by 327
  9. 2022Simple Open-Vocabulary Object Detection with Vision Transformerscited by 304
  10. 2023Delta Denoising Scorecited by 121
  11. 2021Everything's Talkin': Pareidolia Face Reenactmentcited by 10
  12. 2024Seeing Faces in Things: A Model and Dataset for Pareidoliacited by 4

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  3. 2024PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priors
  4. 2026Forecasting Motion in the Wild
  5. 2025TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
  6. 2023Exploring the Feasibility of Generating Realistic 3D Models of Endangered Species Using DreamGaussian: An Analysis of Elevation Angle's Impact on Model Generation
  7. 2024Diffusion Models in 3D Vision: A Survey
  8. 2024Multistable Shape from Shading Emerges from Patch Diffusion