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VersaGauss: A Versatile Framework for Generating Multiphase Dynamics with 3D Gaussians
Ruijie Su, Lingxiao Yang, Xiaohua Xie, Jianhuang Lai
TL;DR
Existing 3D Gaussian physics methods mainly focus on solid objects and single-phase interactions, leaving diverse multiphase coupling difficult. VersaGauss unifies generation, simulation, and rendering from a few images using particle pruning and CMPM, with harmonic interpolation and Gaussian evolution for fluid appearance. Experiments demonstrate interactions among fluids, rubber, sand, snow, and other materials, while the authors note that material types are primarily manually specified.
Problem
Existing approaches mainly target solid-object dynamics and single-phase collision interactions, making coupled interactions across diverse phases difficult.
Method
VersaGauss generates dynamic 3D Gaussian scenes from a few images using particle pruning, CMPM, harmonic interpolation, and Gaussian evolution.
Results
Extensive experiments demonstrate multiphase interactions among materials including fluid, rubber, sand, and snow, with physically accurate dynamics in the reported ball–snowman comparison.
Takeaways & Limitations
The framework supports unified generation, simulation, and rendering of realistic multiphase dynamics within a 3D Gaussian representation.
Takeaways & Limitations
Current material types are primarily manually specified, with automatic inference deferred to future work.
Abstract
from arXiv · showhide
Recent progress has been made in 3D Gaussian representation for reconstruction, generation, and physical simulation. However, current approaches mainly concentrate on physics-based dynamic generation of solid objects and only handle single-phase collision interactions. We introduce VersaGauss, a unified framework for generation, simulation, and rendering that supports versatile physics-based dynamic generation, particularly for multiphase interactions. Our system takes a few images as input and produces a realistic, physics-driven 3D dynamic scene with multiple objects. To optimize the Gaussian kernel distribution, we develop a particle pruning algorithm. We also propose the Coupled Multiphase Point Method (CMPM) to effectively model and generate multiphase interactions. Additionally, harmonic interpolation within CMPM and a Gaussian evolution strategy are introduced to achieve realistic fluid rendering. Extensive experiments demonstrate that our framework can simulate interactions among various materials such as fluid, rubber, sand, snow, and others. Code is available at https://github.com/Elowen-surj/VersaGauss.
I. INTRODUCTION
VersaGauss builds on explicit 3D Gaussian representations to generate, simulate, and render physics-based dynamics across multiphase interactions. It addresses limited material diversity, weak coupling, and fluid-rendering artifacts with particle pruning and coupled simulation strategies.
- Motivation: 3D Gaussians can be treated as particles with physical attributes, enabling particle-based simulation of physically plausible 4D content.This avoids visual artifacts caused by converting between representations in conventional simulation-to-rendering workflows.
- Research Gap: Existing 3DGS physics methods support limited materials and mostly weak coupling within single-phase or same-material interactions.Accurate coupling across phases with different densities and constitutive behaviors remains difficult.
- Research Gap: Fluid particles can cross object boundaries, producing blurry or noisy renderings during fluid-involved interactions.The introduction identifies boundary crossing as a specific rendering problem.
- Approach: VersaGauss combines generation, simulation, and rendering for coupled multiphase dynamics from a few images.The framework segments and reassembles objects, refines Gaussian distributions through particle pruning, and links particles to materials.
- Contributions: CMPM, harmonic interpolation, Gaussian evolution, and particle pruning support multiphase simulation, fluid rendering, and computational efficiency.The framework demonstrates interactions involving materials including fluid, rubber, sand, and snow, with competitive generation efficiency and scalability.
B. Multiphysics Simulation Methods
Multiphysics simulation methods couple diverse materials either through specialized solvers or unified formulations. VersaGauss uses an MPM-based CMPM formulation to target coupled interactions across fluids, rubber, sand, snow, and other phases.
- Multiphysics Simulation Methods: Multiphysics simulation targets coupled interactions among diverse materials such as fluids, elastomers, and plasticity.Existing approaches use either coupled specialized solvers or unified formulations.
- Coupled Solvers: Specialized-solver coupling offers flexibility but requires complex interfaces and stabilization, while weak coupling can introduce artifacts and strong coupling is costly.The passage contrasts flexibility with interface and computational challenges.
- Unified Formulations: MPM facilitates momentum exchange between phases and can incorporate additional contact models, offering a practical balance of stability and flexibility.The method still faces challenges including numerical viscosity and artificial adhesion.
- Research Gap: Most existing methods handle limited materials and primarily single-phase interactions, motivating coupled generation across fluids, rubber, sand, snow, and beyond.This scope is the stated goal for the proposed approach.
- Unified Formulations: MPM stores material state on particles, computes forces and momentum updates on a background grid, and transfers updated quantities back.This particle-grid cycle supports large deformations and phase changes.
IV. VERSAGAUSS
VersaGauss constructs a multiphase dynamic scene from segmented images and 3D Gaussian objects, then simulates and renders it through CMPM. Its pipeline includes particle pruning, material-aware coupling, and fluid-specific rendering refinement.
- Preprocessing: Given one or more images, VersaGauss segments foreground objects with SAM and reconstructs each object as a 3D Gaussian representation.The pipeline then interprets the Gaussians as a particle cloud for simulation.
- Preprocessing: Objects are normalized, rescaled, repositioned into a canonical simulation domain, and later transformed back for rendering.This preserves their relative size and layout from the original image.
- Preprocessing: Particle pruning reduces the particle count for small objects while preserving appearance and lowering computational cost.The introduction identifies pruning as a strategy for refining Gaussian distributions and improving efficiency.
- Simulation: CMPM simulates coupled multiphase interactions using weakly compressible fluids and phase-aware coupling with per-phase grid velocities.The method assigns material-dependent behavior through phase-specific simulation treatment.
- Rendering: Harmonic interpolation transfers spherical-harmonic color attributes through the MPM grid, while Gaussian evolution refines fluid covariance and opacity before rendering.These operations support smooth fluid appearance and novel-view rendering of the dynamic Gaussian scene.
B. Multiple Object Interation Generation.
VersaGauss reduces Gaussian particle cost and links particles to object-level materials for multiphase simulation. Its CMPM preserves phase-specific velocities and uses specialized coupling and color interpolation for physically plausible interactions and smooth fluid rendering.
- Particle Pruning: Particle pruning reduces unnecessary Gaussian density while preserving surface structure for small objects.Surface and interior particles are pruned at different ratios, and remaining covariances are enlarged to compensate for reduced density.
- Particle-Object-Material Link: Object-level material links reduce memory and let particles select constitutive models for material-specific motion and deformation.Each particle queries its linked material entry during MPM updates to compute stresses.
- Coupled Multiphase Point Method: CMPM maintains separate grid velocities for each phase, avoiding the interface blending caused by conventional MPM velocity averaging.The parallel phase-specific update does not noticeably increase computation time.
- Coupled Multiphase Point Method: CMPM uses distinct update rules for fluid-fluid and fluid-sand interactions, incorporating diffusion for fluids and drag-dependent coupling for fluid-sand dynamics.The fluid diffusion coefficient τ controls diffusion velocity, while the fluid-sand drag coefficient depends on sand porosity, permeability, and gravity.
- Fluid Rendering: Kernel-weighted spherical-harmonic color interpolation transfers color through grid nodes and back to particles for smoother fluid surfaces.Color attributes are updated alongside material properties such as mass and velocity.
C. Rendering
VersaGauss adjusts fluid Gaussian covariance and opacity during rendering to handle large particle motion, boundary crossing, and oversaturated splash colors. It tracks deformation volume through Jw and uses isotropic kernel scaling, opacity reduction, and time-dependent Gaussian evolution to improve fluid appearance.
- Rendering Challenges: Fluid particles can cross object boundaries and move substantially, producing blurry or noisy renderings during dynamic events.High-opacity particles and limited semi-transparency support can also cause oversaturated splash colors.
- Fluid Gaussian Adjustment: Pre-rendering covariance and opacity adjustments normalize particle volumes and prevent artifacts from extreme covariance values.Particle volumes are estimated from voxel size and particle count, normalized throughout simulation, and refined using deformation.
- Fluid Gaussian Adjustment: Fluid deformation tracks the determinant Jw of the deformation gradient rather than the full gradient used for solids.The determinant provides the deformation quantity used to update fluid kernels over time.
- Fluid Gaussian Evolution: A first-order mapping transforms each fluid Gaussian kernel, with isotropic scaling maintaining spherical particle shape and minimizing artifacts.The resulting formulation produces time-dependent 3D Gaussian Splatting for deformed fluid particles.
- Fluid Gaussian Evolution: Opacity is scaled by s_opacity,oi in [0.005, 0.02] to model fluid semi-transparency while preserving dense interior appearance.The adjustment improves splash realism and reduces noise.
A. Results
VersaGauss demonstrates versatile multiphase dynamics across diverse materials and scenarios, including snow, fluids, granular materials, elastic objects, and sand. Compared with PhysGaussian and video-generation models, it produces physically accurate ball–snowman interactions.
- A. Results: VersaGauss simulates interactions among snow, fluids, granular materials, elastic objects, and sand across multiple dynamic scenarios.Showcases include deformation, fracturing, mixing, floating, sinking, flow transport, collapse, and fluid-driven sand motion.
- A. Results: Snow and elastic-object interactions produce elastic deformation of a monkey doll and fracturing of a snowball.
- A. Results: Fluids with differing densities and diffusion velocities gradually mix, while granular flow transports objects according to density and container tilt.
- A. Results: In ball–snowman comparisons, VersaGauss achieves physically accurate multiphase dynamics, unlike PhysGaussian, Wan2.1, and CogVideoX-5B.
B. Ablation Studies
The studies examine rendering adjustments and particle pruning. Gaussian evolution, harmonic interpolation, opacity scaling, and pruning affect visual quality or computational cost.
- B. Ablation Studies: Fixed covariance causes blurry artifacts, fixed harmonics produce sandy fluid surfaces, and fixed opacity leads to oversaturated renderings.These variants isolate the effects of covariance, spherical-harmonics interpolation, and opacity scaling during fluid rendering.
- B. Ablation Studies: The ablations evaluate Gaussian evolution and harmonic interpolation as pre-rendering adjustments for fluid Gaussian kernels.
- B. Ablation Studies: Covariance normalization by particle radii and deformation is separately evaluated for rendering fluid splashes in Appendix A.
- B. Ablation Studies: Particle pruning significantly reduces computational cost while preserving image quality according to PSNR comparisons.Table I compares Gaussian kernel count, simulation time, rendering time, and PSNR with and without pruning.
C. Additional Studies
Additional studies vary fluid diffusion and sand permeability to analyze multiphase behavior. Higher fluid diffusion promotes gradual mixing, while greater sand permeability increases sand sinking with fluid flow.
- C. Additional Studies: The fluid–fluid study uses two phases with density ratio white:brown = 1:2 and different diffusion coefficients τ.
- C. Additional Studies: Higher diffusion coefficient τ facilitates gradual mixing, whereas lower τ produces stratification from density contrast.
- C. Additional Studies: Increasing sand permeability pE enhances viscous coupling from sand particles and causes more sand to sink with fluid flow.
- C. Additional Studies: Appendix B provides further details on fluid bulk modulus k, CMPM energy stability, and automatic physical-parameter optimization using video diffusion and SDS loss.
VI. DISCUSSION
VersaGauss is presented as a unified framework for realistic multiphase dynamics in a 3D Gaussian representation. Its current material types are primarily manually specified.
- VI. DISCUSSION: VersaGauss unifies generation, simulation, and rendering for high-quality, realistic multiphase interactions within a 3D Gaussian representation.
- VI. DISCUSSION: Current material types are primarily manually specified, motivating future automatic inference of materials and parameters.The authors propose integrating large language models and video-generation models for complex scenes.