Extending 3D Gaussian Splatting (3DGS) to 4D physical simulation remains challenging. Based on the Material Point Method (MPM), existing methods either rely on manual parameter tuning or distill dynamics from video diffusion models, limiting the generalization and optimization efficiency. Recent attempts using LLMs/VLMs suffer from a text/image-to-3D perceptual gap, yielding unstable physics behavior. In addition, they often ignore the surface structure of 3DGS, leading to implausible motion. We propose FastPhysGS, a fast and robust framework for physics-based dynamic 3DGS simulation:(1) Instance-aware Particle Filling (IPF) with Monte Carlo Importance Sampling (MCIS) to efficiently populate interior particles while preserving geometric fidelity; (2) Bidirectional Graph Decoupling Optimization (BGDO), an adaptive strategy that rapidly optimizes material parameters predicted from a VLM. Experiments show FastPhysGS achieves high-fidelity physical simulation in 1 minute using only 7 GB runtime memory, outperforming prior works with broad potential applications.

翻译：将3D高斯泼溅（3DGS）扩展至四维物理仿真仍具挑战性。基于物质点法（MPM）的现有方法要么依赖手动参数调整，要么从视频扩散模型中蒸馏动力学信息，限制了泛化能力与优化效率。近期利用LLMs/VLMs的尝试受限于文本/图像到三维的感知鸿沟，导致物理行为不稳定。此外，这些方法常忽略3DGS的表面结构，从而产生不合理的运动。我们提出FastPhysGS——一个快速鲁棒的基于物理的动态3DGS仿真框架：（1）采用蒙特卡洛重要性采样（MCIS）的实例感知粒子填充（IPF）方法，在保持几何保真度的同时高效生成内部粒子；（2）双向图解耦优化（BGDO）——一种自适应策略，可快速优化从VLM预测得到的材料参数。实验表明，FastPhysGS仅需1分钟运行时间与7GB内存即可实现高保真物理仿真，性能优于现有方法，具有广泛的应用潜力。