SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning

A paper submitted to arXiv on 23 Oct 2026 introduces SceneBloom, an agentic framework that pairs diffusion-based visual layout sampling with contract-guided 3D compilation to generate simulation-ready indoor environments for embodied-agent training. Across five room types, SceneBloom achieved the highest visual quality and physical stability among compared methods and was the only method with no detected mesh collisions or out-of-bounds objects, with layouts closer to the distribution of real rooms than all baselines. Object navigation policies trained in SceneBloom's generated scenes reached a higher success rate on held-out real scans than policies trained on an equal number of real training scans, and code is available on GitHub.

Computer Science Computer Vision and Pattern Recognition Submitted on 23 Oct 2026 Title:SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning View PDF http://arxiv.org/pdf/2610.32978v1 Abstract:Training generalizable embodied agents requires realistic, varied, and simulation-ready indoor environments. Language-guided methods support semantic planning and object-level reasoning, but precise spatial grounding remains difficult. Visual generative models offer rich priors over plausible arrangements, yet their outputs require geometric reasoning to become physically valid 3D scenes. We introduce SceneBloom, an agentic framework that combines visual layout sampling with contract-guided 3D compilation. A hierarchical sampler uses diffusion priors to generate furniture arrangements within functional zones. A compiler then uses relations extracted from each proposal to guide geometric fitting and local pose correction. Across five room types, SceneBloom attains the highest visual quality and physical stability among the compared methods and is the only method with no detected mesh collisions and out-of-bounds objects. Its layouts are also closer to the distribution of real rooms than those of all compared baselines. Object navigation policies trained in these realistic scenes achieve a higher success rate in held-out real scans than those trained on an equal number of real training scans. Code is available at this https URL https://github.com/Tinaliu0123/scenebloom . Additional Features Current browse context: cs.CV References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .