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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/2410.11908 |
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| _version_ | 1866910652111519744 |
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| author | Qin, Sizhong He, Chengyu Chen, Qiaoyun Yang, Sen Liao, Wenjie Gu, Yi Lu, Xinzheng |
| author_facet | Qin, Sizhong He, Chengyu Chen, Qiaoyun Yang, Sen Liao, Wenjie Gu, Yi Lu, Xinzheng |
| contents | The generation and editing of floor plans are critical in architectural planning, requiring a high degree of flexibility and efficiency. Existing methods demand extensive input information and lack the capability for interactive adaptation to user modifications. This paper introduces ChatHouseDiffusion, which leverages large language models (LLMs) to interpret natural language input, employs graphormer to encode topological relationships, and uses diffusion models to flexibly generate and edit floor plans. This approach allows iterative design adjustments based on user ideas, significantly enhancing design efficiency. Compared to existing models, ChatHouseDiffusion achieves higher Intersection over Union (IoU) scores, permitting precise, localized adjustments without the need for complete redesigns, thus offering greater practicality. Experiments demonstrate that our model not only strictly adheres to user specifications but also facilitates a more intuitive design process through its interactive capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11908 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans Qin, Sizhong He, Chengyu Chen, Qiaoyun Yang, Sen Liao, Wenjie Gu, Yi Lu, Xinzheng Human-Computer Interaction Artificial Intelligence The generation and editing of floor plans are critical in architectural planning, requiring a high degree of flexibility and efficiency. Existing methods demand extensive input information and lack the capability for interactive adaptation to user modifications. This paper introduces ChatHouseDiffusion, which leverages large language models (LLMs) to interpret natural language input, employs graphormer to encode topological relationships, and uses diffusion models to flexibly generate and edit floor plans. This approach allows iterative design adjustments based on user ideas, significantly enhancing design efficiency. Compared to existing models, ChatHouseDiffusion achieves higher Intersection over Union (IoU) scores, permitting precise, localized adjustments without the need for complete redesigns, thus offering greater practicality. Experiments demonstrate that our model not only strictly adheres to user specifications but also facilitates a more intuitive design process through its interactive capabilities. |
| title | ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2410.11908 |