EfM: Simulation-Free Elastic Parameter Estimation from Observed Motion

Reconstructing an object's 3D geometry and appearance captures how it looks, but its material properties are needed to simulate how it responds to new forces. Existing video-based material estimators either rely on costly inverse simulation or use feed-forward predictors whose material estimates are not explicitly constrained to satisfy the observed dynamics. We present EfM (Elasticity from Motion), which directly recovers the elastic parameters of freely moving objects from multi-view video without running a simulator. EfM makes this direct solve possible with two components: an F-GNN that infers unobserved volumetric deformation from visible surface motion, and a space-time weak formulation that avoids explicit acceleration estimation from observed surface trajectories. Together, they reduce material estimation to a single linear solve for Young's modulus and Poisson's ratio. On PAC-NeRF and Spring-Gaus-Diverse, EfM reduces Young's modulus error by 7.5$\times$ and 3.3$\times$ over the best baseline, respectively, predicts unseen future motion most accurately, and fits a sequence in 0.74s, three orders of magnitude faster than inverse simulation.