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_id	ecaade2022_65
id	ecaade2022_65
authors	Halici, Süheyla Müge and Gül, Leman Figen
year	2022
title	Utilizing Generative Adversarial Networks for Augmenting Architectural Massing Studies: AI-assisted Mixed Reality
doi	https://doi.org/10.52842/conf.ecaade.2022.1.323
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 323–330
summary	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.
keywords	Hybrid Design Environment, Dynamic Design Models, Mixed Reality, Generative Adversarial Networks, Image-to-Image Translation, Tracking
series	eCAADe
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last changed	2024/04/22 07:10

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