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Computer Science > Graphics

arXiv:2503.05484 (cs)
[Submitted on 7 Mar 2025 ]

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

Title: 解耦高斯:基于物理的交互对象-场景解耦

Authors:Miaowei Wang, Yibo Zhang, Rui Ma, Weiwei Xu, Changqing Zou, Daniel Morris
Abstract: 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/.
Abstract: 我们提出了DecoupledGaussian,一种新的系统,该系统能够将静态物体与其在野外视频中捕获的接触表面解耦,这是进行基于牛顿物理的真实模拟的关键前提。 与之前专注于合成数据或沿接触表面的弹性抖动的方法不同,这些方法会阻止物体完全脱离或独立移动,DecoupledGaussian允许显著的位置变化而不受初始接触表面的限制。 认识到当前2D修复工具在恢复3D位置方面的局限性,我们的方法提出了联合泊松场,在分离后修复和扩展物体和接触场景的高斯分布。 这通过多雕刻策略来补充,以优化物体的几何形状。 我们的系统能够实现由用户指定冲量驱动的解耦运动、碰撞和断裂的真实模拟,支持多个场景内部及跨场景的复杂交互。 我们通过全面的用户研究和定量基准验证了DecoupledGaussian。 该系统增强了现实环境中与物体和场景的数字交互,对虚拟现实、机器人技术和自动驾驶等行业有益。 我们的项目页面为:https://wangmiaowei.github.io/DecoupledGaussian.github.io/。
Comments: CVPR2025 Accepted
Subjects: Graphics (cs.GR) ; Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2503.05484 [cs.GR]
  (or arXiv:2503.05484v1 [cs.GR] for this version)
  https://doi.org/10.48550/arXiv.2503.05484
arXiv-issued DOI via DataCite

Submission history

From: Miaowei Wang [view email]
[v1] Fri, 7 Mar 2025 14:54:54 UTC (9,403 KB)
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