Global Graph-Validated Optimization for VLM-based 3D Indoor Scene Generation

We study open-vocabulary 3D indoor layout generation, whichsynthesizes diverse and physically plausible indoor scenes from unla-beled 3D assets given free-form language instructions. Recent text-guidedlayout generation methods leverage large language models (LLMs) andvision-language models (VLMs) to synthesize structured scenes directlyfrom text descriptions. However, most existing approaches model inter-asset relations implicitly or rely on local pairwise constraints during lo-cal optimization. Such formulations are misaligned with the global andhighly non-convex feasible layout space, often producing locally plausiblebut globally inconsistent or physically infeasible scenes. To address theselimitations, we introduce a graph-based intermediate representation thatdecouples semantic coherence and physical feasibility, and propose ahybrid search-and-refinement strategy to generate indoor layouts withglobal semantic consistency and physical feasibility. First, we proposeGlobal Semantic Verification (GSV), which represents scenes as struc-tured scene graphs and enforces semantic constraints through rule-basedgraph verification. This explicit structural validation prunes contradic-tory configurations and produces a globally consistent semantic scaffoldfor scene generation. Second, we introduce Global Physical FeasibilitySearch (GPFS), a hybrid optimization framework that combines evo-lutionary search for global exploration with gradient-based refinementfor local exploitation. GPFS reduces dependence on VLM-proposed ini-tialization and improves robustness in highly non-convex and discon-tinuous feasible spaces. Together, GSV and GPFS shift layout genera-tion from local relational modeling and initialization-sensitive optimiza-tion toward globally consistent reasoning and exploration. Experimentsdemonstrate that our method achieves state-of-the-art performance onopen-vocabulary 3D indoor layout generation, improving both semanticconsistency and physical plausibility.