| id |
caadria2024_243 |
| authors |
Han, Zhen, Li, Xiaoqian, Yuan, Ye and Stouffs, Rudi |
| year |
2024 |
| title |
GRAPH2PIX: A Generative Model for Converting Room Adjacency Relationships into Layout Images |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.139
|
| 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. 139–148 |
| summary |
With the advancement of mathematics and computer science, deep learning-based generative design for floorplans has increasingly garnered attention among researchers. This study proposes a graph-based deep learning model, graph2pix (G2P) to synthesize floorplans guided by user-defined constraints. By incorporating room area and type information into the nodes of the graph, G2P can generate floorplans tailored to specific user requirements. It contains three sub-models: the Translator, Generator, and Discriminator. The Translator serves as the foundational 08641080 |
| keywords |
Floor Plan Generative Design, Graph to Image Generative, Graph Neural Network, Room Information Addition, Conditional Generative Adversarial Network. |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,147,516 bytes) |
| references |
Content-type: text/plain
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 Graph2plan: Learning floorplan generation from layout graphs
, ACM Transactions on Graphics (TOG), 39(4), 118-1. https://doi.org/10.1145/3386569.3392391
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, ACM Transactions on Graphics (TOG), 38(6), 1-12. https://doi.org/10.1145/3355089.3356556
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| last changed |
2024/11/17 22:05 |
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