This paper presents a learning-based method for the simulation of rich contact deformations on reduced deformation models. Previous works learn deformation models for specific pairs of objects; we lift this limitation by designing a neural model that supports general rigid collider shapes. We do this by formulating a novel collider descriptor that characterizes local geometry in a region of interest. The paper shows that the learning-based deformation model can be trained on a library of colliders, but it accurately supports unseen collider shapes at runtime. We showcase our method on interactive dynamic simulations with animation of rich deformation detail, manipulation and exploration of untrained objects, and augmentation of contact information suitable for high-fidelity haptics.
@inproceedings{romero2023contactdescriptorlearning,
author = {Romero, Cristian and Casas, Dan and Chiaramonte, Maurizio M. and Otaduy, Miguel A.},
title = {Learning Contact Deformations with General Collider Descriptors},
articleno = {77},
booktitle = {SIGGRAPH Asia 2023 Conference Papers},
publisher = {Association for Computing Machinery},
year = {2023}
}