A Proposed Framework of Manifesting 3D Forms from 2D Architectural AI Outputs

For much of recent history, the thought of machines being in any semblance within reach of human cognition has been one of imagination. But now, technology has speedily caught up to these aspirations, and society faces the daunting task of accepting, learning, and utilizing such tools. Specifically in the lens of design and architecture, what needs to be done at this point is to (1) reflect on how technology currently plays a role within it, and (2) determine how technology should continue to impact it.

In this thesis, I will survey artificial intelligence applications dedicated to image generation and explore how they serve the purposes of architectural design. In particular, I will utilize recently popular text-to-image applications, such as Midjourney alongside existing GANs. Given these current various outputs available from machine learning tools, there is yet no current framework or methodology defined for architects to utilize these tools to manifest a 3D structure out of them. I will propose a framework for how architects can use various generative architectural outputs, namely massing and programming, to realize an actual 3D structure that serves as inspiration in the early design stages. However, besides being a guideline for individuals who wish to use these tools, ultimately this framework could also be referenced as a preliminary model for those working on the continuous improvement of 3D machine learning algorithms. After all, as the design process inevitably becomes more digitized, it is important to maintain a humanistic and an authentically architectural approach within the algorithms that oversee our design outputs.