# DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interaction

arXiv: https://arxiv.org/abs/2503.05484 · HTML: https://mlsanity.com/paper/2503.05484/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 108 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 30 of 58)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [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)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [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)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://mlsanity.com/paper/2303.12789/) (2023)
- [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)
- [SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion](https://mlsanity.com/paper/2403.12008/) (2024)
- [SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes](https://mlsanity.com/paper/2312.14937/) (2023)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction](https://mlsanity.com/paper/2406.06521/) (2024)
- [DreamVideo: Composing Your Dream Videos with Customized Subject and Motion](https://mlsanity.com/paper/2312.04433/) (2023)
- [Shape As Points: A Differentiable Poisson Solver](https://mlsanity.com/paper/2106.03452/) (2021)
- [Unsupervised Point Cloud Pre-Training via Occlusion Completion](https://mlsanity.com/paper/2010.01089/) (2020)
- [VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality](https://mlsanity.com/paper/2401.16663/) (2024)
- [PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation](https://mlsanity.com/paper/2409.18964/) (2024)
- [Segment Any 3D Gaussians](https://mlsanity.com/paper/2312.00860/) (2023)
- [PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification](https://mlsanity.com/paper/2303.05512/) (2023)

## Built on by (most cited first, 17 of 17)

- [Duplex-GS: Proxy-Guided Weighted Blending for Real-Time Order-Independent Gaussian Splatting](https://mlsanity.com/paper/2508.03180/) (2025)
- [IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion](https://mlsanity.com/paper/2508.13153/) (2025)
- [MotionPhysics: Learnable Motion Distillation for Text-Guided Simulation](https://mlsanity.com/paper/2601.00504/) (2026)
- [DSG-World: Learning a 3D Gaussian World Model from Dual State Videos](https://mlsanity.com/paper/2506.05217/) (2025)
- [CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus](https://mlsanity.com/paper/2509.04859/) (2025)
- [RecurGS: Interactive Scene Modeling via Discrete-State Recurrent Gaussian Fusion](https://mlsanity.com/paper/2512.18386/) (2025)
- [CA-World: Multi-Object Counterfactual Alignment for Efficient Interactive-Ready Reconstruction](https://mlsanity.com/paper/2605.30239/) (2026)
- [Streaming Video Generation with Streaming Force Control](https://mlsanity.com/paper/2606.07508/) (2026)
- [Scene-Level Heterogeneous Physics Simulation with 3D Gaussian Splats](https://mlsanity.com/paper/2606.21753/) (2026)
- [MeGAS: Thermomechanical Dynamic Gaussian Splatting for Thermophysical Scene Editing](https://mlsanity.com/paper/2606.23455/) (2026)
- [Occlusion-Robust Multi-Object Decoupling for Physics-Based Robotic Interaction](https://mlsanity.com/paper/2606.29303/) (2026)
- [FractureFields: Contact-Aware Binary Multi-Field Transfer for Fractured 3D Gaussian Simulation](https://mlsanity.com/paper/2608.28982/) (2026)
- [LightFuse: Relightable Interactive Gaussian Scene Reconstruction via Multi-Scan Fusion and 2D Gaussian Ray Tracing](https://mlsanity.com/paper/2608.29269/) (2026)
- [PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians](https://mlsanity.com/paper/2609.07174/) (2026)
- [SAMV-DUSt3R: Instance-Centric 3D Scene Decoupling from Sparse Multi-Views](https://mlsanity.com/paper/2609.11279/) (2026)
- [Robot-GST: geometry-aware spatial-temporal robot policy representation and evaluation](https://mlsanity.com/paper/2609.33872/) (2026)
- [PhysForge: Material-Controllable 3D Reconstruction with Separable Components](https://mlsanity.com/paper/2610.13681/) (2026)

## Abstract (arXiv)

We present DecoupledGaussian, a novel system that decouples static objects from their contacted surfaces captured in-the-wild videos, a key prerequisite for realistic Newtonian-based physical simulations. Unlike prior methods focused on synthetic data or elastic jittering along the contact surface, which prevent objects from fully detaching or moving independently, DecoupledGaussian allows for significant positional changes without being constrained by the initial contacted surface. Recognizing the limitations of current 2D inpainting tools for restoring 3D locations, our approach proposes joint Poisson fields to repair and expand the Gaussians of both objects and contacted scenes after separation. This is complemented by a multi-carve strategy to refine the object's geometry. Our system enables realistic simulations of decoupling motions, collisions, and fractures driven by user-specified impulses, supporting complex interactions within and across multiple scenes. We validate DecoupledGaussian through a comprehensive user study and quantitative benchmarks. This system enhances digital interaction with objects and scenes in real-world environments, benefiting industries such as VR, robotics, and autonomous driving. Our project page is at: https://wangmiaowei.github.io/DecoupledGaussian.github.io/.