FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918111306842112 |
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| author | Yin, Jun Zeng, Pengyu Zhong, Jing Li, Peilin Zhang, Miao Luo, Ran Lu, Shuai |
| author_facet | Yin, Jun Zeng, Pengyu Zhong, Jing Li, Peilin Zhang, Miao Luo, Ran Lu, Shuai |
| contents | In the architectural design process, floor plan generation is inherently progressive and iterative. However, existing generative models for floor plans are predominantly end-to-end generation that produce an entire pixel-based layout in a single pass. This paradigm is often incompatible with the incremental workflows observed in real-world architectural practice. To address this issue, we draw inspiration from the autoregressive 'next token prediction' mechanism commonly used in large language models, and propose a novel 'next room prediction' paradigm tailored to architectural floor plan modeling. Experimental evaluation indicates that FPDS demonstrates competitive performance in comparison to diffusion models and Tell2Design in the text-to-floorplan task, indicating its potential applicability in supporting future intelligent architectural design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21562 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction Yin, Jun Zeng, Pengyu Zhong, Jing Li, Peilin Zhang, Miao Luo, Ran Lu, Shuai Computation and Language Artificial Intelligence Hardware Architecture In the architectural design process, floor plan generation is inherently progressive and iterative. However, existing generative models for floor plans are predominantly end-to-end generation that produce an entire pixel-based layout in a single pass. This paradigm is often incompatible with the incremental workflows observed in real-world architectural practice. To address this issue, we draw inspiration from the autoregressive 'next token prediction' mechanism commonly used in large language models, and propose a novel 'next room prediction' paradigm tailored to architectural floor plan modeling. Experimental evaluation indicates that FPDS demonstrates competitive performance in comparison to diffusion models and Tell2Design in the text-to-floorplan task, indicating its potential applicability in supporting future intelligent architectural design. |
| title | FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction |
| topic | Computation and Language Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2506.21562 |