Parallel Multigrid for Nonlinear Cloth Simulation


Accurate high-resolution simulation of cloth is a highly desired computational tool in graphics applications. As single resolution simulation starts to reach the limit of computational power, we believe the future of cloth simulation is in multi-resolution simulation. In this paper, we explore nonlinearity, adaptive smoothing, and parallelization under a full multigrid (FMG) framework. The foundation of this research is a novel nonlinear FMG method for unstructured meshes. To introduce nonlinearity into FMG, we propose to formulate the smoothing process at each resolution level as the computation of a search direction for the original high-resolution nonlinear optimization problem. We prove that our nonlinear FMG is guaranteed to converge under various conditions and we investigate the improvements to its performance. We present an adaptive smoother which is used to reduce the computational cost in the regions with low residuals already. Compared to normal iterative solvers, our nonlinear FMG method provides faster convergence and better performance for both Newton’s method and Projective Dynamics. Our experiment shows our method is efficient, accurate, stable against large time steps, and friendly with GPU parallelization. The performance of the method has a good scalability to the mesh resolution, and the method has good potential to be combined with multi-resolution collision handling for real-time simulation in the future.

Computer Graphics Forum (Pacific Graphics). Best Paper Award.