Portal de Periódicos da CAPES

Contact-centric deformation learning

2022; Association for Computing Machinery; Volume: 41; Issue: 4 Linguagem: Inglês

10.1145/3528223.3530182

1557-7368

Cristian Romero, Dan Casas, Maurizio M. Chiaramonte, Miguel Á. Otaduy,

Computer Graphics and Visualization Techniques

We propose a novel method to machine-learn highly detailed, nonlinear contact deformations for real-time dynamic simulation. We depart from previous deformation-learning strategies, and model contact deformations in a contact-centric manner. This strategy shows excellent generalization with respect to the object's configuration space, and it allows for simple and accurate learning. We complement the contact-centric learning strategy with two additional key ingredients: learning a continuous vector field of contact deformations, instead of a discrete approximation; and sparsifying the mapping between the contact configuration and contact deformations. These two ingredients further contribute to the accuracy, efficiency, and generalization of the method. We integrate our learning-based contact deformation model with subspace dynamics, showing real-time dynamic simulations with fine contact deformation detail.