Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning
Xingjian Ran, Yixuan Li, Linning Xu, Mulin Yu, Bo Dai
Realistic 3D indoor scene synthesis is crucial for Embodied AI and digital content creation. However, achieving high fidelity, strong generalization and precise controllability remains challenging due to complex semantic and physical constraints. Existing methods follow two paradigms: (1) Training models on layout datasets to directly generate numerical 3D layouts, which often generalize poorly to unseen room types; (2) Using LLMs/VLMs to produce open-vocabulary intermediate representations (e.g., scene graphs) followed by constraint-based optimization, improving plausibility but sacrificing flexibility due to predefined rules. Both approaches struggle to adapt to fine-grained user requirements. We introduce DirectLayout, a framework that directly generates numerical 3D layouts from text descriptions, without relying on intermediate representations and constrained optimization. DirectLayout decomposes the generation into three stages: producing a Bird's-Eye View (BEV) layout, lifting it into 3D space, and refining object placements for plausibility. To enable explicit spatial reasoning and help the model grasp basic principles of object placement, we employ Chain-of-Thought (CoT) activation based on the 3D-Front dataset. Additionally, we design CoT-Grounded Generative Layout Reward to enhance generalization and spatial planning. During inference, DirectLayout addresses asset-layout mismatches via Iterative Asset-Layout Alignment through in-context learning. Extensive experiments demonstrate that DirectLayout achieves impressive semantic consistency, generalization and physical plausibility.