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

Jun Yin, Pengyu Zeng, Jing Zhong, Peilin Li, Miao Zhang, Ran Luo, Shuai Lu

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.

External IDs:dblp:journals/corr/abs-2506-21562