An Implicit Frictional Contact Solver for Adaptive Cloth Simulation

Cloth dynamics plays an important role in the visual appearance of moving characters. Properly accounting for contact and friction is of utmost importance to avoid cloth-body and cloth-cloth penetration and to capture typical folding and stick-slip behavior due to dry friction. We present here the first method able to account for cloth contact with exact Coulomb friction, treating both cloth self-contacts and contacts occurring between the cloth and an underlying character. Our key contribution is to observe that for a nodal system like cloth, the frictional contact problem may be formulated based on velocities as primary variables, without having to compute the costly Delassus operator. Then, by reversing the roles classically played by the velocities and the contact impulses, conical complementarity solvers of the literature can be adapted to solve for compatible velocities at nodes. To handle the full complexity of cloth dynamics scenarios, we have extended this base algorithm in two ways: first, towards the accurate treatment of frictional contact at any location of the cloth, through an adaptive node refinement strategy; second, towards the handling of multiple constraints at each node, through the duplication of constrained nodes and the adding of pin constraints between duplicata. Our method allows us to handle the complex cloth-cloth and cloth-body interactions in full-size garments with an unprecedented level of realism compared to former methods, while maintaining reasonable computational timings.

• Preprint [PDF, 9.1MB]
• Video [MP4, 88.8MB]
• Source Code

The authors would like to thank Liam Toran for his early thoughts as an intern on a new quartic solve adapted to the primal formulation, David Harmon for sharing reference code for inelastic projection, and Mickaël Ly for help with creating renderings and the supplementary video. We are also grateful to the anonymous reviewers for their valuable comments. This work was supported in part by the NSF grant #1657089, the ERC grant GEM (StG-2014-639139), and by the LabEx PERSYVAL-Lab (ANR-11-LABX-0025-01) funded by the French program Investissement d’Avenir.