Existing inverse physics methods recover physical parameters from multi-view videos, where geometric constraints across views resolve scale and 3D structure. In monocular settings, however, such constraints are absent, leading to severe scale ambiguity, inaccurate geometry, and weak coupling between appearance optimization and physical simulation. We propose MonoPhysics, a framework for monocular inverse physics estimation of deformable objects using differentiable MPM simulation and 3D Gaussian Splatting, which jointly optimizes geometry, appearance, and physical parameters from a single camera view. We address these challenges through three visual-physical bridges: global scale alignment, physics-aware geometry refinement, and a differentiable position map, which together enable accurate optimization from monocular observations alone. We evaluate on Vid2Sim and our new dataset of elastic and plastic objects, showing that MonoPhysics outperforms existing baselines in monocular settings and achieves performance comparable to multi-view baselines using only a single camera. Our project page is available at https://daniel03c1.github.io/MonoPhysics/

翻译：现有的逆物理方法从多视角视频中恢复物理参数，利用跨视角的几何约束解决尺度与三维结构问题。然而在单目设置中，此类约束缺失，导致严重的尺度模糊性、不精确的几何结构，以及外观优化与物理模拟之间的弱耦合。我们提出MonoPhysics——一种基于可微分MPM模拟与三维高斯泼溅技术的单目可形变物体逆物理估计框架，可从单一相机视角联合优化几何、外观与物理参数。通过三种视觉-物理桥梁应对这些挑战：全局尺度对齐、物理感知几何精化以及可微分位置图，三者共同实现对单目观测的精准优化。我们在Vid2Sim数据集及自建的弹性/塑性物体新数据集上评估，结果表明MonoPhysics在单目设置中优于现有基线，且仅使用单相机即可达到与多视角基线相媲美的性能。项目页面详见https://daniel03c1.github.io/MonoPhysics/