用户输入文字即可生成城市街景，让公众参与设计
RECITYGEN -- Interactive and Generative Participatory Urban Design Tool with Latent Diffusion and Segment Anything
- 结合文本提示与交互式分割生成城市街景
- 北京试点中用户生成方案符合公共偏好
- 适合城市规划师与公众共同参与设计
城市设计深刻影响公共空间与社区参与。传统自上而下的方法常忽视公众意见，导致设计愿景与现实脱节。近年来，数字工具如城市信息建模和增强现实已推动更广泛的参与式设计。深度学习与潜在扩散模型进一步降低了设计生成门槛，为参与式城市设计带来新机遇。本文提出RECITYGEN，结合先进潜在扩散模型与交互式语义分割，允许用户通过文本提示生成多样化的城市街景图像。在北京一项城市更新试点项目中，用户利用RECITYGEN提出改进建议。尽管存在局限性，该工具已展现出与公众偏好高度契合的潜力，标志着向更动态、包容的城市规划方法转变。项目源码可在RECITYGEN GitHub获取。
原文摘要 · Abstract (English)
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.
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