Recent advances in Gaussian Splatting have enabled fast, high-fidelity 3D scene generation, yet these methods remain purely visual and lack an understanding of how shapes behave in the physical world. We introduce Physics-Guided 3D Gaussian Splatting (PG-3DGS), a framework that couples differentiable physics simulation with 3D Gaussian representations to generate 3D structures satisfying physics functionalities. By allowing physical objectives to guide the shape optimization process alongside visual losses, our approach produces geometries that are not only photometrically accurate but also physically functional. The model learns to adjust shapes so that the generated objects exhibit physically meaningful behaviors, for example, teapots that can pour and airplanes that can generate lift, without sacrificing visual quality. Experiments on pouring and aerodynamic lift tasks show that PG-3DGS improves physical functionality while preserving visual quality. In addition to simulation gains, bench-top physical lift tests with 3D-printed aircraft (Cessna, B-2 Spirit, and paper plane) under identical airflow conditions show higher scale-measured lift for PG-3DGS, generated structures than an appearance-matching baseline in all three cases. Our unified framework connects appearance-based reconstruction with physics-based reasoning, enabling end-to-end generation of 3D structures that both look realistic and function correctly.

翻译：三维高斯泼溅的最新进展实现了快速、高保真的三维场景生成，然而这些方法仍停留在纯视觉层面，缺乏对形状在物理世界中行为方式的理解。我们提出物理引导三维高斯泼溅（PG-3DGS），这是一个将可微物理仿真与三维高斯表示相耦合的框架，用于生成满足物理功能性的三维结构。通过允许物理目标与视觉损失共同引导形状优化过程，我们的方法能生成在光度精度与物理功能性方面均表现优异的几何体。该模型学习调整形状，使得生成的物体展现出具有物理意义的行为——例如可倒水的茶壶和能产生升力的飞机——同时不牺牲视觉质量。在倒水和气动升力任务上的实验表明，PG-3DGS在保持视觉质量的同时提升了物理功能性。除仿真增益外，在相同气流条件下对三维打印飞行器（塞斯纳、B-2幽灵及纸飞机）进行的台架物理升力测试显示，在所有三种案例中，PG-3DGS生成的结构均比外观匹配基线方法产生了更高的尺度测量升力。我们统一的框架将基于外观的重建与基于物理的推理相连接，实现了既具备真实外观又能正确运行的三维结构的端到端生成。