GenTwin: Part-Aware Generalized Dynamics Modeling with Neural Anisotropic Springs

Learning physically realistic object dynamics from real-world interactions is important for modeling the physical world. Existing methods typically adopt category-specific formulations for articulated and deformable dynamics, limiting their generalization to heterogeneous objects with both rigid and deformable parts. To address this, we introduce GenTwin, a part-aware framework that models generalized dynamics from real-world interaction observations within a common spring-mass representation, decomposing object dynamics into intra- and inter-part dynamics. For intra-part dynamics, we introduce Part-Adaptive Spring Configuration to adapt stiffness and topology to heterogeneous part rigidity using material priors and motion-derived rigidity. For inter-part dynamics, we connect neighboring parts with Neural Anisotropic Spring Connections, whose part-pair Neural Oriented Stiffness Fields parameterize continuous directional stiffness to capture relative motions from flexible limb swinging to constrained articulation. Experiments on 18 real-world scenarios spanning deformable, articulated, and heterogeneous objects demonstrate consistently improved geometric accuracy, reducing future prediction Chamfer distance by approximately 28.6%, 27.3%, and 50.0%, respectively, over the best competing category-specific methods.