Automated 3D scene generation is pivotal for applications spanning virtual reality, digital content creation, and Embodied AI. While computer graphics prioritizes aesthetic layouts, vision and robotics demand scenes that mirror real-world complexity which current data-driven methods struggle to achieve due to limited unstructured training data and insufficient spatial and physical modeling. We propose SPREAD, a diffusion-based framework that jointly learns spatial and physical relationships through a graph transformer, explicitly conditioning on posed scene point clouds for geometric awareness. Moreover, our model integrates differentiable guidance for collision avoidance, relational constraint, and gravity, ensuring physically coherent scenes without sacrificing relational context. Our experiments on 3D-FRONT and ProcTHOR datasets demonstrate state-of-the-art performance in spatial-relational reasoning and physical metrics. Moreover, \ours{} outperforms baselines in scene consistency and stability during pre- and post-physics simulation, proving its capability to generate simulation-ready environments for embodied AI agents.

翻译：自动化三维场景生成对于虚拟现实、数字内容创作和具身智能等应用至关重要。尽管计算机图形学优先考虑美学布局，但视觉和机器人领域需要能够反映现实世界复杂性的场景——当前数据驱动方法因缺乏非结构化训练数据以及空间与物理建模不足而难以实现。我们提出SPREAD，一种通过图变换器联合学习空间与物理关系的扩散框架，其显式条件化于带姿态的场景点云以实现几何感知。此外，模型集成了用于碰撞避免、关系约束和重力的可微引导，在保持关系上下文的同时确保物理一致性场景。我们在3D-FRONT和ProcTHOR数据集上的实验表明，本方法在空间关系推理和物理指标上达到最先进性能。此外，相比于基线方法，本文方法在物理仿真前后均展现出更优的场景一致性和稳定性，证明了其生成可仿真环境以支持具身智能体的能力。