Architectural Intelligence
Harnessing AI for Design Intent
How can the real-time masking and layering of AI-generated imagery be used to effectively realize and refine design intent?
This thesis investigates and develops state-of-the-art technologies in the field of generative content and examines how designers can leverage inherent training biases and input data to swiftly generate textures and imagery for use within the design process.
Ultimately, the developed workflows aid in formulating a cohesive project vision from early stages. By combining proceduralization, simplified modeling techniques, and intelligent masking, the design process accelerates. This not only enables the creation of high-quality renders but more importantly, encourages a nuanced approach to design thinking.
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What if the designer of a project has no experience with complex 3D software and digital tools. Or they prefer to design with physical models in a tangible way. In any case, the goal is to model quickly and freely with less concern for craft and more of an emphasis on rapidly capturing design ideas in their head. How can the designer still make full use of this technology and material information as feedback towards design decisions?
By overlaying a photo captured on my phone of the physical model with the site image, then keying out the yellow background, the model can be placed in context.
From there, a pair of images can be generated: a canny edge detection and an AI-generated depth map. This set of information, in tandem with the original color data, is all the information necessary to produce a final rendering.
Generated maps from model photo
Model photo vs generated render
