This project focuses on developing a generative AI framework to realistically simulate and visualize the long-term aging of architectural façades. Unlike existing generative models that often present idealized, pristine textures, this tool emphasizes the realistic deterioration of materials due to environmental exposure. By incorporating material aging algorithms and data on climate conditions, the tool aim to bridge a critical gap in existing design processes, allowing architects and designers to anticipate how different materials will weather over time.
The tool is interactive, providing real-time feedback to designers as they apply textures to their models. By adjusting environmental parameters such as moisture, sunlight, and pollution, users can visualize texture evolution dynamically and experiment with different aging scenarios. This feature enhances decision-making by offering a predictive understanding of material behavior, helping designers refine their choices and improve the long-term resilience and aesthetic quality of their projects.
(PROJECT IN PROGRESS...)
