| id |
caadria2021_389 |
| authors |
del Campo, Matias |
| year |
2021 |
| title |
Architecture,Language and AI - Language,Attentional Generative Adversarial Networks (AttnGAN) and Architecture Design |
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 211-220 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.211
|
| summary |
The motivation to explore Attentional Generative Adversarial Networks (AttnGAN) as a design technique in architecture can be found in the desire to interrogate an alternative design methodology that does not rely on images as starting point for architecture design, but language. Traditionally architecture design relies on visual language to initiate a design process, wither this be a napkin sketch or a quick doodle in a 3D modeling environment. AttnGAN explores the information space present in programmatic needs, expressed in written form, and transforms them into a visual output. The key results of this research are shown in this paper with a proof-of-concept project: the competition entry for the 24 Highschool in Shenzhen, China. This award-winning project demonstrated the ability of GraphCNN to serve as a successful design methodology for a complex architecture program. In the area of Neural Architecture, this technique allows to interrogate shape through language. An alternative design method that creates its own unique sensibility. |
| keywords |
Artificial Intelligence; Machine Learning; Artificial Neural Networks; Semiotics; Design Methodology |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (18,298,038 bytes) |
| references |
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