Kavli Affiliate: Xiang Zhang
| First 5 Authors: Yu Yang, Yu Yang, , ,
| Summary:
Interactive world models that simulate object dynamics are crucial for
robotics, VR, and AR. However, it remains a significant challenge to learn
physics-consistent dynamics models from limited real-world video data,
especially for deformable objects with spatially-varying physical properties.
To overcome the challenge of data scarcity, we propose PhysWorld, a novel
framework that utilizes a simulator to synthesize physically plausible and
diverse demonstrations to learn efficient world models. Specifically, we first
construct a physics-consistent digital twin within MPM simulator via
constitutive model selection and global-to-local optimization of physical
properties. Subsequently, we apply part-aware perturbations to the physical
properties and generate various motion patterns for the digital twin,
synthesizing extensive and diverse demonstrations. Finally, using these
demonstrations, we train a lightweight GNN-based world model that is embedded
with physical properties. The real video can be used to further refine the
physical properties. PhysWorld achieves accurate and fast future predictions
for various deformable objects, and also generalizes well to novel
interactions. Experiments show that PhysWorld has competitive performance while
enabling inference speeds 47 times faster than the recent state-of-the-art
method, i.e., PhysTwin.
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