Abstract
Existing 3D scene generation methods often struggle to model the complex logical dependencies and physical constraints between objects, limiting their ability to adapt to dynamic and realistic environments. We propose CausalStruct, a novel framework that embeds causal reasoning into 3D scene generation. Utilizing large language models (LLMs), We construct causal graphs where nodes represent objects and attributes, while edges encode causal dependencies and physical constraints. CausalStruct iteratively refines the scene layout by enforcing causal order to determine the placement order of objects and applies causal intervention to adjust the spatial configuration according to physics-driven constraints, ensuring consistency with textual descriptions and real-world dynamics. The refined scene causal graph informs subsequent optimization steps, employing a Proportional-Integral-Derivative(PID) controller to iteratively tune object scales and positions. Our method uses text or images to guide object placement and layout in 3D scenes, with 3D Gaussian Splatting and Score Distillation Sampling improving shape accuracy and rendering stability. Extensive experiments show that CausalStruct generates 3D scenes with enhanced logical coherence, realistic spatial interactions, and robust adaptability.
Abstract (translated)
现有的三维场景生成方法通常难以模拟物体之间的复杂逻辑依赖和物理约束，这限制了它们适应动态和现实环境的能力。我们提出了一种名为CausalStruct的新框架，该框架将因果推理嵌入到3D场景生成中。通过使用大型语言模型（LLMs），我们构建了因果图，其中节点代表对象及其属性，而边则编码因果依赖性和物理约束。CausalStruct 通过强制执行因果顺序来迭代地精炼场景布局以确定物体的放置顺序，并采用因果干预方法根据物理学驱动的约束调整空间配置，确保与文本描述和现实世界动态的一致性。精炼后的场景因果图指导后续优化步骤，利用比例-积分-微分（PID）控制器迭代调优对象的比例和位置。我们的方法使用文本或图像来引导3D场景中的物体放置和布局，并通过3D高斯点投影和评分蒸馏采样提高形状精度和渲染稳定性。广泛的实验表明，CausalStruct 能够生成具有增强逻辑连贯性、逼真空间交互以及强大适应性的三维场景。
URL
https://arxiv.org/abs/2509.15249
