Yuhan Liu, Lianhui Qin, Shenji Wan · Indoor scene generation

SceneBloom

SceneBloom is an agentic framework for generating realistic, simulation-ready indoor rooms for embodied learning, described in an arXiv paper by Yuhan Liu, Lianhui Qin and Shenji Wan. It combines visual layout sampling with contract-guided 3D compilation. A VLM plans functional zones, FLUX.1-dev with asset LoRA adapters samples each zone's arrangement as an image, and a VLM extracts relation contracts from each proposal. A hierarchical compiler then fits the zones, assembles the room and refines poses to restore those contracts. Against Holodeck, SceneWeaver and SAGE on 50 scenes across five room types, SceneBloom had no scenes with mesh collisions or out-of-bounds objects (the others: 78.0%, 82.0% and 78.0% with collisions) and 75.3% physically stable objects (35.1%, 45.3% and 45.1%). It takes 2.7 minutes per layout, about 27× faster than SAGE. A SemExp object-navigation policy trained in SceneBloom houses raised success in held-out real homes from 29.7% to 35.3% on average, 1.7× the gain from training on the same number of real scans. Contract guidance cut contract violations from 37.45% to 4.25% in the ablation. The page does not state a licence or code release.