Utilizing Generative Adversarial Networks for Augmenting Architectural Massing Studies: AI-assisted Mixed Reality

Halıcı S. M., Gül L. F.

40th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2022, Ghent, Belgium, 13 - 16 September 2022, vol.1, pp.323-330 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1
  • City: Ghent
  • Country: Belgium
  • Page Numbers: pp.323-330
  • Keywords: Dynamic Design Models, Generative Adversarial Networks, Hybrid Design Environment, Image-to-Image Translation, Mixed Reality, Tracking
  • Istanbul Technical University Affiliated: Yes


© 2022, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.A technique for architectural massing studies in Mixed Reality (MR) is described. Generative Adversarial Networks let an object appear to have a different material than it actually has. The benefits during design are twofold. From one side the congruence between shape and material are subject to verification in real-time. From the other side, the designer is liberated from the usual restrictions and biases as to shape that are inevitable due to the mechanical properties of a mock-up. This is referred to as artificial intelligence assisted MR (AI-A MR) in this work. The technique consists of two steps: Based on preparing synthetic data in Rhino/Grasshopper to be trained with an image-to-image translation model and implemented to the trained model in MR design environment. Next to the practical merits, a contribution of the work with respect to MR methodology is that it exemplifies the solution of some persistent tracking and registration problems.