Core Concepts
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.
Abstract
This comprehensive review explores the extensive applications of generative AI in architectural design, a trend that has benefited from the rapid development of deep generative models. The article first provides an in-depth introduction to the principles and evolution of various generative AI models, with a focus on Diffusion Models, 3D Generative Models, and Foundation Models.
The review then delves into the application of generative AI in different stages of the architectural design process, including:
Architectural Preliminary 3D Forms Design: Generative AI facilitates the generation of preliminary 3D forms based on input parameters, classification analysis, and the use of 1D text or 2D image data as generation conditions. It also enables the redesign and evaluation of 3D models.
Architectural Plan Design: Generative AI is used to generate floor plans, functional space layouts, spatial sequences, and environmental performance evaluations based on 2D images and other design inputs.
Architectural Structural System Design: Generative AI supports the generation and optimization of structural systems, including load-bearing elements and material selection.
Detailed and Optimization Design of Architectural 3D Forms: Generative AI enhances the refinement and optimization of 3D architectural forms, leveraging techniques like implicit functions and neural radiance fields.
Architectural Facade Design: Generative AI enables the generation and manipulation of architectural facades, incorporating elements like windows, textures, and ornamental details.
Architectural Imagery Expression: Generative AI revolutionizes the creation of architectural imagery, including 2D images, videos, and 3D models, empowering architects to convey their design visions more effectively.
The review also highlights the potential future applications of generative AI in architectural design, such as text-to-image, text-to-video, and text-to-3D model generation, as well as the integration of human-centric design principles.
Stats
Architectural design may encompass multiple themes and scopes, with each project having distinct design requirements and individual styles, leading to diversity and complexity in design approaches.
The number of research papers using Generative AI technology in different architectural design steps reveals the development trends within each subfield, as illustrated in Figure 2(a). Most research is concentrated in the area of architectural plan design. Research in preliminary 3D form design of architecture and architectural image expression has rapidly increased in the past two years.
The most used generative AI techniques are illustrated in Fig 2(b). In computer science, many studies focus on GAN and VAE, while research on DDPM, LDM, and GPT is in the initial stages. The situation is the same in architecture.
Quotes
"Generative Artificial Intelligence (AI) has pioneered new methodological paradigms in architectural design, significantly expanding the innovative potential and efficiency of the design process."
"Diffusion Models achieved state-of-the-art performance in various content generation tasks such as text-to-image and text-to-3D-models."
"The marked trend of research growth indicates an increasing inclination within the architectural design community towards embracing generative AI, thereby catalyzing a shared enthusiasm for research."