SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning
Paper recorded by Signals 4 on 2026-10-23 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26
Category: cs.AI · 人工智能 · first seen 2026-10-26
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 intro
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Cite this page: SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning: the #161 most recent of 960 cs.AI papers we have recorded (as of 2026-10-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/scenebloom-agentic-indoor-environment-generation-with-realistic-and-diverse-layo.html
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