RECITYGEN -- Interactive and Generative Participatory Urban Design Tool with Latent Diffusion and Segment Anything

TL;DR

RECITYGEN is an innovative urban design tool that combines latent diffusion models and interactive segmentation to enable participatory, text-driven creation of urban environment visuals, fostering more inclusive planning.

Contribution

The paper introduces RECITYGEN, a novel interactive tool integrating state-of-the-art generative models with semantic segmentation for participatory urban design.

Findings

RECITYGEN effectively generates variational street view images from text prompts.

Pilot in Beijing showed alignment with public preferences.

The tool enhances inclusivity in urban planning processes.

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.