PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable
  Objects from Videos
Authors: name: Hanxiao Jiang, name: Hao-Yu Hsu, name: Kaifeng Zhang, name: Hsin-Ni Yu, name: Shenlong Wang, name: Yunzhu Li

Creating a physical digital twin of a real-world object has immense potential
in robotics, content creation, and XR. In this paper, we present PhysTwin, a
novel framework that uses sparse videos of dynamic objects under interaction to
produce a photo- and physically realistic, real-time interactive virtual
replica. Our approach centers on two key components: (1) a physics-informed
representation that combines spring-mass models for realistic physical
simulation, generative shape models for geometry, and Gaussian splats for
rendering; and (2) a novel multi-stage, optimization-based inverse modeling
framework that reconstructs complete geometry, infers dense physical
properties, and replicates realistic appearance from videos. Our method
integrates an inverse physics framework with visual perception cues, enabling
high-fidelity reconstruction even from partial, occluded, and limited
viewpoints. PhysTwin supports modeling various deformable objects, including
ropes, stuffed animals, cloth, and delivery packages. Experiments show that
PhysTwin outperforms competing methods in reconstruction, rendering, future
prediction, and simulation under novel interactions. We further demonstrate its
applications in interactive real-time simulation and model-based robotic motion
planning.