RECITYGEN -- 交互式和生成式参与式城市设计工具与潜在扩散与分割任何物
RECITYGEN -- Interactive and Generative Participatory Urban Design Tool with Latent Diffusion and Segment Anything
AI总结：
RECITYGEN通过结合潜在扩散模型和交互式语义分割，为城市设计提供交互式生成工具，帮助公众参与城市更新项目。
摘要：
城市设计深刻影响着公共空间和社区参与。传统自上而下的方法往往忽视公众意见，导致设计愿望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进步，使更多利益相关者能够参与城市设计过程。此外，深度学习和潜在扩散模型降低了设计生成的障碍，为参与式城市设计提供了更多机会。结合最先进的潜在扩散模型与交互式语义分割，我们提出RECITYGEN，这是一种新型工具，允许用户通过文本提示交互式地创建城市环境的变分街景图像。在北京的一项试点项目中，用户使用RECITYGEN为正在进行的城市复兴项目提出改进建议。尽管存在一些局限性，RECITYGEN在与公众偏好一致方面显示出显著潜力，表明一种向更加动态和包容的城市规划方法转变的趋势。该项目的源代码可在RECITYGEN GitHub上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
