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[2602.17117] i-PhysGaussian: Implicit Physical Simulation for 3D Gaussian Splatting
Summary
The paper introduces i-PhysGaussian, a novel framework for implicit physical simulation that enhances 3D Gaussian Splatting by minimizing momentum-balance residuals, improving stability and accuracy in simulations.
Why It Matters
This research addresses limitations in current 3D simulation techniques, particularly in handling complex materials and dynamic movements. By improving stability and reducing sensitivity to time steps, it has significant implications for industries relying on accurate physical simulations, such as engineering and gaming.
Key Takeaways
- i-PhysGaussian couples 3D Gaussian Splatting with an implicit Material Point Method.
- The framework reduces time-step sensitivity, enhancing simulation stability.
- It maintains structural coherence and smooth motion in complex scenarios.
- The method allows for larger time steps compared to traditional explicit methods.
- This approach can significantly benefit industries requiring precise physical simulations.
Computer Science > Machine Learning arXiv:2602.17117 (cs) [Submitted on 19 Feb 2026] Title:i-PhysGaussian: Implicit Physical Simulation for 3D Gaussian Splatting Authors:Yicheng Cao, Zhuo Huang, Yu Yao, Yiming Ying, Daoyi Dong, Tongliang Liu View a PDF of the paper titled i-PhysGaussian: Implicit Physical Simulation for 3D Gaussian Splatting, by Yicheng Cao and 5 other authors View PDF HTML (experimental) Abstract:Physical simulation predicts future states of objects based on material properties and external loads, enabling blueprints for both Industry and Engineering to conduct risk management. Current 3D reconstruction-based simulators typically rely on explicit, step-wise updates, which are sensitive to step time and suffer from rapid accuracy degradation under complicated scenarios, such as high-stiffness materials or quasi-static movement. To address this, we introduce i-PhysGaussian, a framework that couples 3D Gaussian Splatting (3DGS) with an implicit Material Point Method (MPM) integrator. Unlike explicit methods, our solution obtains an end-of-step state by minimizing a momentum-balance residual through implicit Newton-type optimization with a GMRES solver. This formulation significantly reduces time-step sensitivity and ensures physical consistency. Our results demonstrate that i-PhysGaussian maintains stability at up to 20x larger time steps than explicit baselines, preserving structural coherence and smooth motion even in complex dynamic transitions. Subjects:...