用人类感知反馈优化扩散模型，让虚拟城市更新更真实可信。
URSimulator: Human-Perception-Driven Prompt Tuning for Enhanced Virtual Urban Renewal via Diffusion Models
- 结合人类感知反馈，迭代优化街景图像局部区域。
- 安全、美观、活力评分提升分别达17.60%、31.15%、28.82%。
- 适合城市规划、政策制定者用于虚拟场景评估与决策。
解决城市物理紊乱（如废弃建筑、垃圾、杂乱植被、涂鸦）对社区安全、福祉和心理状态的负面影响至关重要。城市更新是通过改造这些被忽视和衰败区域来改善城市环境与居民生活质量的过程。有效更新能显著提升环境吸引力与宜居性。然而，现有研究缺乏可量化评估并可视化更新影响的模拟工具，常依赖主观判断。此类工具对制定有效策略至关重要，能清晰展示潜在变化及其影响。本文提出一种新框架，利用人类感知反馈模拟街道环境改善。我们开发了一种提示调优方法，将文本驱动的Stable Diffusion与人类感知反馈结合，迭代编辑街景图像局部区域，使其更契合人们对美感、活力和安全性的感知。实验表明，该框架显著提升了城市环境感知：安全提升17.60%，美丽度提升31.15%，活力感提升28.82%；相比之下，先进方法DiffEdit仅实现2.31%、11.87%、15.84%的提升。我们在多个虚拟场景中应用该框架，包括邻里改善、建筑重建、绿地扩展及社区花园建设，结果验证了其在模拟城市更新中的有效性，为城市规划与政策制定提供宝贵洞见。
原文摘要 · Abstract (English)
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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