# Efficient Physics Simulation for 3D Scenes via MLLM-Guided Gaussian Splatting

arXiv: https://arxiv.org/abs/2411.12789 · HTML: https://mlsanity.com/paper/2411.12789/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 56 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 30 of 39)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://mlsanity.com/paper/2303.12789/) (2023)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Gaussian Grouping: Segment and Edit Anything in 3D Scenes](https://mlsanity.com/paper/2312.00732/) (2023)
- [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023)
- [Recognize Anything: A Strong Image Tagging Model](https://mlsanity.com/paper/2306.03514/) (2023)
- [Differentiable Surface Splatting for Point-based Geometry Processing](https://mlsanity.com/paper/1906.04173/) (2019)
- [DreamGaussian4D: Generative 4D Gaussian Splatting](https://mlsanity.com/paper/2312.17142/) (2023)
- [PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation](https://mlsanity.com/paper/2404.13026/) (2024)
- [VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality](https://mlsanity.com/paper/2401.16663/) (2024)
- [PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification](https://mlsanity.com/paper/2303.05512/) (2023)
- [Animate124: Animating One Image to 4D Dynamic Scene](https://mlsanity.com/paper/2311.14603/) (2023)
- [ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation](https://mlsanity.com/paper/2403.08321/) (2024)
- [Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians](https://mlsanity.com/paper/2403.09434/) (2024)
- [Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion](https://mlsanity.com/paper/2406.04338/) (2024)

## Built on by (most cited first, 30 of 32)

- [PhysGM: Large Physical Gaussian Model for Feed-Forward 4D Synthesis](https://mlsanity.com/paper/2508.13911/) (2025)
- [PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback](https://mlsanity.com/paper/2606.08688/) (2026)
- [From 2D to 3D Cognition: A Brief Survey of General World Models](https://mlsanity.com/paper/2506.20134/) (2025)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)
- [FastPhysGS: Accelerating Physics-based Dynamic 3DGS Simulation via Interior Completion and Adaptive Optimization](https://mlsanity.com/paper/2602.01723/) (2026)
- [Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints](https://mlsanity.com/paper/2603.11606/) (2026)
- [NeuROK: Generative 4D Neural Object Kinematics](https://mlsanity.com/paper/2605.30347/) (2026)
- [Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning](https://mlsanity.com/paper/2505.14938/) (2025)
- [DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos](https://mlsanity.com/paper/2605.09586/) (2026)
- [CP4D: Compositional Physics-aware 4D Scene Generation](https://mlsanity.com/paper/2606.09187/) (2026)
- [Moving Light Adaptive Colonoscopy Reconstruction via Illumination-Attenuation-Aware 3D Gaussian Splatting](https://mlsanity.com/paper/2510.18739/) (2025)
- [OrthoPhys: Physically Plausible Video Generation with Orthogonal-View Geometry Guidance](https://mlsanity.com/paper/2603.18639/) (2026)
- [MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos](https://mlsanity.com/paper/2605.19386/) (2026)
- [Learning Action-Conditional and Object-Centric Gaussian Splatting World Models for Rigid Objects](https://mlsanity.com/paper/2606.01950/) (2026)
- [Adaptive Volumetric Mechanical Property Fields Invariant to Resolution](https://mlsanity.com/paper/2606.18231/) (2026)
- [muSync-GS: Physics-Synchronized Driving Video Synthesis for Weather and Geometric Road Hazards](https://mlsanity.com/paper/2608.04412/) (2026)
- [WildGHand: Learning Anti-Perturbation Gaussian Hand Avatars from Monocular In-the-Wild Videos](https://mlsanity.com/paper/2602.20556/) (2026)
- [PhyGenHOI: Physically-Aware 4D Generation of Dynamic Human-Object Interactions](https://mlsanity.com/paper/2605.30268/) (2026)
- [Scene-Level Heterogeneous Physics Simulation with 3D Gaussian Splats](https://mlsanity.com/paper/2606.21753/) (2026)
- [Occlusion-Robust Multi-Object Decoupling for Physics-Based Robotic Interaction](https://mlsanity.com/paper/2606.29303/) (2026)
- [DR-GS: Physically-Based Deformable and Relightable 2D Gaussians](https://mlsanity.com/paper/2606.29379/) (2026)
- [Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations](https://mlsanity.com/paper/2607.07962/) (2026)
- [Scene2Sound: Auditory-Grounded Soundscape Generation for 3D Gaussian Worlds](https://mlsanity.com/paper/2608.00463/) (2026)
- [Vision Meets WiFi: Physics-Grounded Estimation of Volumetric Mechanical Properties](https://mlsanity.com/paper/2608.07726/) (2026)
- [KnockGS:interaction-Grounded Calibrationof Physical Gaussian Representations](https://mlsanity.com/paper/2608.27365/) (2026)
- [CapFrame: Text-Instructed Viewpoint Grounding in 3D Gaussian Scenes via Geometric Pseudo Labels](https://mlsanity.com/paper/2608.30342/) (2026)
- [Analytic Dynamics: Learning Physics-Grounded Representation for Fast Intrinsic Dynamics Inference from Monocular Videos](https://mlsanity.com/paper/2608.31025/) (2026)
- [PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians](https://mlsanity.com/paper/2609.07174/) (2026)
- [PhysFlow: Physics-Aware Optical Flow for Motion Controllable Video Generation](https://mlsanity.com/paper/2609.08215/) (2026)
- [SnapPhysics: A Physics-Aware Scene Graph from a Single View for Interactive Mixed Reality Scenes](https://mlsanity.com/paper/2609.19815/) (2026)

## Abstract (arXiv)

Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on video generation models to predict them, which is computationally intensive. In this paper, we rethink the usage of multi-modal large language model (MLLM) in physics-based simulation, and present Sim Anything, a physics-based approach that endows static 3D objects with interactive dynamics. We begin with detailed scene reconstruction and object-level 3D open-vocabulary segmentation, progressing to multi-view image in-painting. Inspired by human visual reasoning, we propose MLLM-based Physical Property Perception (MLLM-P3) to predict mean physical properties of objects in a zero-shot manner. Based on the mean values and the object's geometry, the Material Property Distribution Prediction model (MPDP) model then estimates the full distribution, reformulating the problem as probability distribution estimation to reduce computational costs. Finally, we simulate objects in an open-world scene with particles sampled via the Physical-Geometric Adaptive Sampling (PGAS) strategy, efficiently capturing complex deformations and significantly reducing computational costs. Extensive experiments and user studies demonstrate our Sim Anything achieves more realistic motion than state-of-the-art methods within 2 minutes on a single GPU.