FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction

• URL: http://arxiv.org/abs/2506.21562v2
• Date: Sat, 02 Aug 2025 18:27:22 GMT
• Title: FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction
• Authors: Jun Yin, Pengyu Zeng, Jing Zhong, Peilin Li, Miao Zhang, Ran Luo, Shuai Lu,
• Abstract summary: Existing generative models for floor plans are predominantly end-to-end generation that produce an entire pixel-based layout in a single pass.<n>We propose a novel 'next room prediction' paradigm tailored to architectural floor plan modeling.<n> FPDS demonstrates competitive performance in comparison to diffusion models and Tell2Design in the text-to-floorplan task.
• Score: 25.35768485637194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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