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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.06859 |
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| _version_ | 1866915055417688064 |
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| author | Ibrahim, Mohamed R. Musil, Josef Gallou, Irene |
| author_facet | Ibrahim, Mohamed R. Musil, Josef Gallou, Irene |
| contents | In the domain of architectural design, the foundational essence of creativity and human intelligence lies in the mastery of solving floorplans, a skill demanding distinctive expertise and years of experience. Traditionally, the architectural design process of creating floorplans often requires substantial manual labour and architectural expertise. Even when relying on parametric design approaches, the process is limited based on the designer's ability to build a complex set of parameters to iteratively explore design alternatives. As a result, these approaches hinder creativity and limit discovery of an optimal solution. Here, we present a generative latent diffusion model that learns to generate floorplans for various building types based on building footprints and design briefs. The introduced model learns from the complexity of the inter-connections between diverse building types and the mutations of architectural designs. By harnessing the power of latent diffusion models, this research surpasses conventional limitations in the design process. The model's ability to learn from diverse building types means that it cannot only replicate existing designs but also produce entirely new configurations that fuse design elements in unexpected ways. This innovation introduces a new dimension of creativity into architectural design, allowing architects, urban planners and even individuals without specialised expertise to explore uncharted territories of form and function with speed and cost-effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06859 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating floorplans for various building functionalities via latent diffusion model Ibrahim, Mohamed R. Musil, Josef Gallou, Irene Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition In the domain of architectural design, the foundational essence of creativity and human intelligence lies in the mastery of solving floorplans, a skill demanding distinctive expertise and years of experience. Traditionally, the architectural design process of creating floorplans often requires substantial manual labour and architectural expertise. Even when relying on parametric design approaches, the process is limited based on the designer's ability to build a complex set of parameters to iteratively explore design alternatives. As a result, these approaches hinder creativity and limit discovery of an optimal solution. Here, we present a generative latent diffusion model that learns to generate floorplans for various building types based on building footprints and design briefs. The introduced model learns from the complexity of the inter-connections between diverse building types and the mutations of architectural designs. By harnessing the power of latent diffusion models, this research surpasses conventional limitations in the design process. The model's ability to learn from diverse building types means that it cannot only replicate existing designs but also produce entirely new configurations that fuse design elements in unexpected ways. This innovation introduces a new dimension of creativity into architectural design, allowing architects, urban planners and even individuals without specialised expertise to explore uncharted territories of form and function with speed and cost-effectiveness. |
| title | Generating floorplans for various building functionalities via latent diffusion model |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.06859 |