Scene Generation at Absolute Scale: Utilizing Semantic and Geometric Guidance From Text for Accurate and Interpretable 3D Indoor Scene Generation

We present GuidedSceneGen, a text-to-3D generation frame-work that produces metrically accurate, globally consistent, and semanti-cally interpretable indoor scenes. Unlike prior text-driven methods thatoften suffer from geometric drift or scale ambiguity, our approach main-tains an absolute world coordinate frame throughout the entire generationprocess. Starting from a textual scene description, we predict a global 3Dlayout encoding both semantic and geometric structure, which serves as aguiding proxy for downstream stages. A semantics- and depth-conditionedpanoramic diffusion model then synthesizes 360° imagery aligned with theglobal layout, substantially improving spatial coherence. To explore un-observed regions, we employ a video diffusion model guided by optimizedcamera trajectories that balances coverage and collision avoidance, achiev-ing up to 10× faster sampling compared to exhaustive path exploration.The generated views are fused using 3D Gaussian Splatting, yielding aconsistent and fully navigable 3D scene in absolute scale. GuidedSceneGenenables accurate transfer of object poses and semantic labels from lay-out to reconstruction, and supports progressive scene expansion withoutre-alignment. Quantitative results and a user study demonstrate greater3D consistency and layout plausibility compared to recent text-to-3Dbaselines. Project page: https://d3ixi.github.io/GuidedSceneGen/