| id |
caadria2024_497 |
| authors |
El Mesawy, Mohamed, Zaher, Nawal and El Antably, Ahmed |
| year |
2024 |
| title |
From Topology to Spatial Information: A Computational Approach for Generating Residential Floorplans |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 129–138 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.129
|
| summary |
Multimodal models that combine different media like text, image, audio, and graph have revolutionised the architectural design process, which could provide automated solutions to assist the architects during the early design stages. Recent studies use Graph Neural Networks (GNNs) to learn topological information and Convolution Neural Networks (CNNs) to learn spatial information from floorplans. This paper proposes a deep learning multimodal model incorporating GNNs and the Stable Diffusion model to learn the floorplan's topological and spatial information. The authors trained a Stable Diffusion model on samples from the RPLAN dataset. They used graph embedding for conditional generation and experimented with three approaches to whole-graph embedding techniques. The proposed Stable Diffusion model maps the user input, a graph representing the room types and their relationships, to the output, the predicted floorplans, as a raster image. The Graph2Vec and contrastive learning methods generate superior representational capabilities and yield good and comparable results in both computationally derived scores and evaluations conducted by human assessors, compared to the Graph Encoder-CNN Decoder. |
| keywords |
Floorplan Generation, Deep Generative Models, Multimodal Machine Learning, Graph Neural Networks [Gnns], Representation Learning |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (845,565 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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