Roam2Room: A Unified Floorplan-to-Furnished Framework for Controllable Indoor Scene Generation

Indoor scene generation is crucial for robot simulation andmodern interior design. However, complex layouts together with scarce3D scene data make learning-based generation challenging. Existing meth-ods often rely on hand-crafted rules or focus on isolated sub-tasks (e.g.,floorplan synthesis or single-room furnishing), producing whole-homescenes that lack global coherence, realism, and simulation readiness. Tomitigate these limitations, we propose a unified hierarchical frameworkthat decomposes indoor scene synthesis into controllable stages. First, wecurate a large-scale dataset of 300K real residential floorplans to traina large language model for whole-home floorplan generation. With de-tailed descriptions and a K-D tree–based representation, our method en-ables fine-grained, controllable whole-home floorplan generation. Build-ing upon the generated whole-home floorplan, we leverage image gener-ation models to draft furniture layouts from multi-level roaming view-points, and then generate the layouts of small manipulable objects ondifferent supporting surfaces (e.g. cabinets, desks, and dining tables) forembodied AI simulation. During furniture and object layout generation,a VLM-based refiner iteratively corrects furniture and object placement,and a 3D generative model enables flexible replacement of individualassets. We further attach basic physical attributes and simple surfacetexture and lighting setups to complete the pipeline for embodied AIuse. Experiments and user studies demonstrate that our pipeline pro-duces indoor spaces with greater layout diversity and stronger 3D designappeal, outperforming prior methods on both quantitative and qualita-tive metrics. Finally, alongside our generation pipeline, we will releasethe floorplan dataset and 5K fully furnished scenes to the community.Project page: https://kairos-homeworld.github.io/.