Unified Vector Floorplan Generation via Markup Representation
This work addresses the problem of efficient and accessible spatial design for architecture and computer graphics, offering a general solution for floorplan generation across various conditional tasks.
The paper tackles the challenge of generating diverse and functional residential floorplans under heterogeneous conditions by introducing Floorplan Markup Language (FML), a unified representation, and a transformer-based model, FMLM, which surpasses previous task-specific state-of-the-art methods on the RPLAN dataset.
Automatic residential floorplan generation has long been a central challenge bridging architecture and computer graphics, aiming to make spatial design more efficient and accessible. While early methods based on constraint satisfaction or combinatorial optimization ensure feasibility, they lack diversity and flexibility. Recent generative models achieve promising results but struggle to generalize across heterogeneous conditional tasks, such as generation from site boundaries, room adjacency graphs, or partial layouts, due to their suboptimal representations. To address this gap, we introduce Floorplan Markup Language (FML), a general representation that encodes floorplan information within a single structured grammar, which casts the entire floorplan generation problem into a next token prediction task. Leveraging FML, we develop a transformer-based generative model, FMLM, capable of producing high-fidelity and functional floorplans under diverse conditions. Comprehensive experiments on the RPLAN dataset demonstrate that FMLM, despite being a single model, surpasses the previous task-specific state-of-the-art methods.