A novel AI-assisted architectural design method that generates preliminary architectural designs from massing models, optimizing for daylighting through a diffusion model-based approach.
Deep learning techniques, particularly deep generative models, enable the efficient and diverse generation of virtual architecture to enrich the content and user experiences in the metaverse.
SCAPE combines evolutionary search with generative AI, enabling users to explore creative and high-quality architectural designs inspired by their initial input through a simple point and click interface.
Generative AI technologies, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, are revolutionizing the architectural design process by enhancing efficiency, expanding creative potential, and optimizing design solutions.
A factor graph-based approach that effectively models higher-order spatial constraints to generate floorplan layouts that closely align with user requirements.
This paper presents a novel workflow that utilizes generative AI models to rapidly generate conceptual floorplans, 3D massing models, and architectural renderings from simple sketches, enabling efficient ideation and controlled generation in the early stages of the architectural design process.