VersaGauss: A Versatile Framework for Generating Multiphase Dynamics with 3D Gaussians
- URL: http://arxiv.org/abs/2608.28069v1
- Date: Fri, 28 Aug 2026 08:38:03 GMT
- Title: VersaGauss: A Versatile Framework for Generating Multiphase Dynamics with 3D Gaussians
- Abstract summary: VersaGauss is a unified framework for generation, simulation, and rendering.<n>It supports versatile physics-based dynamic generation, particularly for multiphase interactions.
- Score: 86.3300808181023
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians [34.993124532814186]
PhysMAS is a physics-grounded multi-agent framework for dynamic scene generation.<n>It invokes solver-aware skills to bind identities and profiles to per-particle MPM fields and execute all objects in a shared domain.<n>It achieves better semantic alignment and perceived physical plausibility while requiring less runtime.
arXiv Detail & Related papers (2026-09-07T08:07:05Z) - DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer [62.18680935878919]
We introduce DiffusionHarmonizer, an online generative enhancement framework that transforms renderings into temporally consistent outputs.<n>At its core is a single-step temporally-conditioned enhancer capable of running in online simulators on a single GPU.
arXiv Detail & Related papers (2026-02-27T15:35:30Z) - FastPhysGS: Accelerating Physics-based Dynamic 3DGS Simulation via Interior Completion and Adaptive Optimization [56.17833729527066]
We propose FastPhysGS, a framework for physics-based dynamic 3DGS simulation.<n>FastPhysGS achieves high-fidelity physical simulation in 1 minute using only 7 GB runtime memory.
arXiv Detail & Related papers (2026-02-02T07:00:42Z) - GenCP: Towards Generative Modeling Paradigm of Coupled Physics [12.60448078274613]
GenCP is a novel and principled generative paradigm for coupled multiphysics simulation.<n>We use operator-splitting theory in the space of probability evolution to establish error controllability guarantees for this "conditional-to-joint" sampling scheme.<n>We evaluate our paradigm on a synthetic setting and three challenging multi-physics scenarios to demonstrate both insight and superior application performance of GenCP.
arXiv Detail & Related papers (2026-01-27T12:31:49Z) - GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials [34.530526668727056]
GaussianFluent is a framework for realistic simulation and rendering of dynamic object states.<n>It synthesizes interiors by densifying internal Gaussians guided by generative models.<n>It integrates an optimized Continuum Damage Material Point Method (CD-MPM) to enable brittle fracture simulation at remarkably high speed.
arXiv Detail & Related papers (2026-01-14T07:59:52Z) - GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
arXiv Detail & Related papers (2024-12-23T18:58:17Z) - GASP: Gaussian Splatting for Physic-Based Simulations [3.377471081417809]
Physics simulation is paramount for modeling and utilizing 3D scenes in various real-world applications.<n>Existing models use additional meshing mechanisms, including triangle or tetrahedron meshing, marching cubes, or cage meshes.<n>Alternatively, we can modify the physics-grounded Newtonian dynamics to align with 3D Gaussian components.<n>Our GS for Physics-Based Simulations (GASP) pipeline uses parametrized flat Gaussian distributions.
arXiv Detail & Related papers (2024-09-09T17:28:57Z) - Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering [41.589093951039814]
We integrate physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS.
Our framework is capable of realistically reproducing surface highlights on dynamic fluids and facilitating interactions between scene objects and fluids from new views.
arXiv Detail & Related papers (2024-01-27T06:45:22Z) - Hierarchical Generation of Human-Object Interactions with Diffusion
Probabilistic Models [71.64318025625833]
This paper presents a novel approach to generating the 3D motion of a human interacting with a target object.
Our framework first generates a set of milestones and then synthesizes the motion along them.
The experiments on the NSM, COUCH, and SAMP datasets show that our approach outperforms previous methods by a large margin in both quality and diversity.
arXiv Detail & Related papers (2023-10-03T17:50:23Z) - Learning to Simulate Complex Physics with Graph Networks [68.43901833812448]
We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains.
Our framework---which we term "Graph Network-based Simulators" (GNS)--represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing.
Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.
arXiv Detail & Related papers (2020-02-21T16:44:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.