Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos

Understand

Modeling the dynamics of deformable objects is challenging due to their diverse physical properties and the difficulty of estimating states from limited visual information.

• We address these challenges with a neural dynamics framework that combines object particles and spatial grids in a hybrid representation.
• Our particle-grid model captures global shape and motion information while predicting dense particle movements, enabling the modeling of objects with varied shapes and materials.
• Particles represent object shapes, while the spatial grid discretizes the 3D space to ensure spatial continuity and enhance learning efficiency.