URSimulator: Human-Perception-Driven Prompt Tuning for Enhanced Virtual Urban Renewal via Diffusion Models

• URL: http://arxiv.org/abs/2409.14589v1
• Date: Sun, 22 Sep 2024 20:39:32 GMT
• Title: URSimulator: Human-Perception-Driven Prompt Tuning for Enhanced Virtual Urban Renewal via Diffusion Models
• Authors: Chuanbo Hu, Shan Jia, Xin Li,
• Abstract summary: Urban Physical Disorder negatively impacts safety, well-being, and psychological state of communities. Urban Renewal is the process of revitalizing neglected and decayed areas within a city to improve the physical environment and quality of life for residents. Current research lacks simulation tools that can quantitatively assess and visualize the impacts of renewal efforts. This paper presents a novel framework using human perception feedback to simulate street environment enhancement.
• Score: 10.498291811234726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tackling Urban Physical Disorder (e.g., abandoned buildings, litter, messy vegetation, graffiti) is essential, as it negatively impacts the safety, well-being, and psychological state of communities. Urban Renewal is the process of revitalizing these neglected and decayed areas within a city to improve the physical environment and quality of life for residents. Effective urban renewal efforts can transform these environments, enhancing their appeal and livability. However, current research lacks simulation tools that can quantitatively assess and visualize the impacts of renewal efforts, often relying on subjective judgments. Such tools are crucial for planning and implementing effective strategies by providing a clear visualization of potential changes and their impacts. This paper presents a novel framework addressing this gap by using human perception feedback to simulate street environment enhancement. We develop a prompt tuning approach that integrates text-driven Stable Diffusion with human perception feedback, iteratively editing local areas of street view images to better align with perceptions of beauty, liveliness, and safety. Our experiments show that this framework significantly improves perceptions of urban environments, with increases of 17.60% in safety, 31.15% in beauty, and 28.82% in liveliness. In contrast, advanced methods like DiffEdit achieve only 2.31%, 11.87%, and 15.84% improvements, respectively. We applied this framework across various virtual scenarios, including neighborhood improvement, building redevelopment, green space expansion, and community garden creation. The results demonstrate its effectiveness in simulating urban renewal, offering valuable insights for urban planning and policy-making.

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