Abstract
Urban design profoundly impacts public spaces and community engagement. Traditional top-down methods often overlook public input, creating a gap in design aspirations and reality. Recent advancements in digital tools, like City Information Modelling and augmented reality, have enabled a more participatory process involving more stakeholders in urban design. Further, deep learning and latent diffusion models have lowered barriers for design generation, providing even more opportunities for participatory urban design. Combining state-of-the-art latent diffusion models with interactive semantic segmentation, we propose RECITYGEN, a novel tool that allows users to interactively create variational street view images of urban environments using text prompts. In a pilot project in Beijing, users employed RECITYGEN to suggest improvements for an ongoing Urban Regeneration project. Despite some limitations, RECITYGEN has shown significant potential in aligning with public preferences, indicating a shift towards more dynamic and inclusive urban planning methods. The source code for the project can be found at RECITYGEN GitHub.
Abstract (translated)
城市设计对公共空间和社区参与有着深远的影响。传统的自上而下的方法往往忽视了公众的意见，导致设计理念与现实之间存在差距。近年来，数字工具（如城市信息建模和增强现实）的发展使更多的利益相关者能够参与到城市设计中来，从而促成了更加参与式的过程。此外，深度学习和潜在扩散模型降低了生成设计方案的门槛，为更为参与式的城市设计提供了更多机会。 我们提出了一种新型工具——RECityGen，结合了最新的潜在扩散模型与互动语义分割技术，使用户能够使用文本指令交互地创建城市的街道视图图像。在北京的一个试点项目中，参与者利用RECityGen提出了关于正在进行的城市再生项目的改进建议。尽管存在一些限制，但RECityGen已经展现出了在满足公众偏好方面的巨大潜力，这表明城市规划方法正朝着更加动态和包容的方向转变。 该项目的源代码可以在RECityGen的GitHub页面上找到。
URL
https://arxiv.org/abs/2602.07057
