FlowPlan: Joint Structural and Geometric Generation of Polygonal Floorplans via Flow Matching

Floorplan generation is an increasingly prominent challenge at the intersection of computer vision, computer graphics, and architecture. Early rule-based heuristics often fail to generalize across diverse, real-world datasets. Recent generative methods have achieved strong performance, but are predominantly evaluated on relatively regular residential layouts and often rely on predefined room semantics, topology, or cardinality. In this work, we introduce FlowPlan, a "generate all" approach that concurrently produces both discrete room semantics and their corresponding continuous geometry. By conditioning the generation process solely on the floor plan boundary, our method eliminates the need for predefined semantic assumptions. We explore various flow matching formulations for discrete variables and evaluate our approach against established baselines on a real-world dataset. Due to our method's flexibility, a single model can be supplied with additional conditioning at test time and thus compared against baselines with and without semantic generation capabilities, demonstrating performance on par with highly specialized models.