MonoPhysics: Estimating Geometry, Appearance, and Physical Parameters from Monocular Videos 文章

ArXiv CS.CV2026-05-29NEWSen作者: Daniel Rho, Jun Myeong Choi, Matthew Thornton, Biswadip Dey, Roni Sengupta

摘要

arXiv:2605.30320v1 Announce Type: new Abstract: 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.