Summary of Urbangenai: Reconstructing Urban Landscapes Using Panoptic Segmentation and Diffusion Models, by Timo Kapsalis
UrbanGenAI: Reconstructing Urban Landscapes using Panoptic Segmentation and Diffusion Models
by Timo Kapsalis
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel workflow combining computer vision and generative artificial intelligence (genAI) to revolutionize urban landscape reconstruction. The prototype application, UrbanGenAI, leverages advanced image segmentation using the OneFormer model and diffusion models through ControlNet for text-to-image generation. Validation results show high performance in object detection and text-to-image generation, with superior IoU and CLIP scores across various urban landscape features. UrbanGenAI has potential applications in design pedagogy, enhancing learning experiences, and participatory planning, facilitating community-driven urban planning. The ongoing development of UrbanGenAI aims to integrate real-time feedback mechanisms and 3D modeling capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using computers to help people design cities. It combines two technologies: computer vision (which helps computers understand pictures) and generative AI (which can create new images). The result is a tool that can help designers make better decisions by showing them different possibilities for city planning. This tool, called UrbanGenAI, has already shown promise in helping people learn about design and working together to plan cities. The researchers are continuing to improve this tool and add more features. |
Keywords
* Artificial intelligence * Image generation * Image segmentation * Object detection
