STARPlan: Stable Topology-Aligned Representations for Autoregressive Vector Floorplan Generation

Graph-constrained vectorized floorplan generation requires connecting unordered input graphs with ordered geometric sequences. We present STARPlan, an autoregressive framework that learns topology representations from complete input bubble graphs and reuses them throughout geometry generation. Through masked graph pretraining, a graph encoder learns node representations independently of geometry generation and is then frozen to provide stable structural conditions for an autoregressive Transformer. A graph-derived generation order establishes correspondence between graph nodes and generated rooms and doors. We find that a simple strategy of reusing the same topology representations as both complete-graph conditions and object-aligned features effectively bridges graph structure and autoregressive sequence modeling. The complete set of node representations supplies global structural context through cross-attention, while the representation of each corresponding node augments the coordinate embeddings of its object. Experiments on RPLAN demonstrate state-of-the-art performance in layout diversity and topology compatibility across mixed-training and unseen-room-count settings, together with improved visual and functional realism in a user study.