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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.20186 |
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| _version_ | 1866913290515382272 |
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| author | Li, Pengzhi Li, Baijuan Li, Zhiheng |
| author_facet | Li, Pengzhi Li, Baijuan Li, Zhiheng |
| contents | Recently, the development of large-scale models has paved the way for various interdisciplinary research, including architecture. By using generative AI, we present a novel workflow that utilizes AI models to generate conceptual floorplans and 3D models from simple sketches, enabling rapid ideation and controlled generation of architectural renderings based on textual descriptions. Our work demonstrates the potential of generative AI in the architectural design process, pointing towards a new direction of computer-aided architectural design. Our project website is available at: https://zrealli.github.io/sketch2arc |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_20186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sketch-to-Architecture: Generative AI-aided Architectural Design Li, Pengzhi Li, Baijuan Li, Zhiheng Computer Vision and Pattern Recognition Recently, the development of large-scale models has paved the way for various interdisciplinary research, including architecture. By using generative AI, we present a novel workflow that utilizes AI models to generate conceptual floorplans and 3D models from simple sketches, enabling rapid ideation and controlled generation of architectural renderings based on textual descriptions. Our work demonstrates the potential of generative AI in the architectural design process, pointing towards a new direction of computer-aided architectural design. Our project website is available at: https://zrealli.github.io/sketch2arc |
| title | Sketch-to-Architecture: Generative AI-aided Architectural Design |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.20186 |