GAMES Webinar 2024 – 318期(布料仿真专题) | Xuan Li(UCLA)，Zhendong Wang(Style3D Research)

【GAMES Webinar 2024-318期】(模拟与动画专题-布料仿真专题)

报告嘉宾：Xuan Li(UCLA)

报告时间：2024年4月12号星期五早上10:00-10:45（北京时间）

报告题目：Subspace-Preconditioned GPU Projective Dynamics with Contact for Cloth Simulation

报告摘要：

We propose an efficient cloth simulation method that combines the merits of two drastically different numerical procedures, namely the subspace integration and parallelizable iterative relaxation. We show those two methods can be organically coupled within the framework of projective dynamics (PD), where both low- and high-frequency cloth motions are effectively and efficiently computed. Our method works seamlessly with the state-of-the-art contact handling algorithm, the incremental potential contact (IPC), to offer the non-penetration guarantee of the resulting animation. Our core ingredient centers around the utilization of subspace for the expedited convergence of Jacobi-PD. This involves solving the reduced global system and smartly employing its precomputed factorization. In addition, we incorporate a time-splitting strategy to handle the frictional self-contacts. Specifically, during the PD solve, we employ a quadratic proxy to approximate the contact barrier. The prefactorized subspace system matrix is exploited in a reduced-space LBFGS. The LBFGS method starts with the reduced system matrix of the rest shape as the initial Hessian approximation, incorporating contact information into the reduced system progressively, while the full-space Jacobi iteration captures high-frequency details. Furthermore, we address penetration issues through a penetration correction step. It minimizes an incremental potential without elasticity using Newton-PCG. Our method can be efficiently executed on modern GPUs. Experiments show significant performance improvements over existing GPU solvers for high-resolution cloth simulation.

讲者简介：

Xuan Li is a PhD student at AIVC Lab, UCLA Department of Mathematics, advised by Prof. Chenfanfu Jiang. He received his M.S. degree in computer science from Stony Brook University and his B.S. degree in mathematics from Tsinghua University. His current research focuses on physics-based simulation and its application in 3D generation/reconstruction and robotics.

个人主页：https://xuan-li.github.io/

报告嘉宾：Zhendong Wang(Style3D Research)

报告时间：2024年4月12号星期五早上10:45-11:30（北京时间）

报告题目：Stable Discrete Bending by Analytic Eigensystem and Orthotropic Geometric Stiffness

报告摘要：

In this paper, we address two limitations of dihedral angle based discrete bending (DAB) models, i.e. the indefiniteness of their energy Hessian and their vulnerability to geometry degeneracies. To tackle the indefiniteness issue, we present novel analytic expressions for the eigensystem of a DAB energy Hessian. Our expressions reveal that DAB models typically have positive, negative, and zero eigenvalues, with four of each, respectively. By using these expressions, we can efficiently project an indefinite DAB energy Hessian as positive semi-definite analytically. To enhance the stability of DAB models at degenerate geometries, we propose rectifying their indefinite geometric stiffness matrix by using orthotropic geometric stiffness matrices with adaptive parameters calculated from our analytic eigensystem. Among the twelve motion modes of a dihedral element, our resulting Hessian for DAB models retains only the desirable bending modes, compared to the undesirable altitude-changing modes of the exact Hessian with original geometric stiffness, all modes of the Gauss-Newton approximation without geometric stiffness, and no modes of the projected Hessians with inappropriate geometric stiffness. Additionally, we suggest adjusting the compression stiffness according to the Kirchhoff-Love thin plate theory to avoid over-compression. Our method not only ensures the positive semidefiniteness but also avoids instability caused by large bending forces atdegenerate geometries. To demonstrate the benefit of our approaches, weshow comparisons against existing methods on the simulation of cloth andthin plates in challenging examples.

讲者简介：

Dr. Wang Zhendong currently is currently a senior researcher at Zhejiang Linctex Digital Technology Co., Ltd. He leads the team responsible for the development of Style3D Physics, a cutting-edge real-time 3D clothing simulation engine. This engine underpins the company’s three flagship products: Style3D Studio, Style3D Fabric, and Style3D Simulator, contributing significantly to their commercial success. Prior to joining Linctex, he received his Ph.D. degree in Engineering from Zhejiang University in December 2018, after obtaining his Bachelor’s degree in Engineering from Wuhan University in 2013. His academic journey includes a year-long visit as a scholar at The Ohio State University in 2017, and an internship as a game physics engine developer at Shenzhen Tencent Technology Co., Ltd., from July to November 2018. His research spans a broad array of topics, including the development of industrial and game physics engines, the digitalization and intelligent manufacturing of clothing, simulations of both soft and rigid bodies, collision detection and handling, numerical simulations, optimization methods, geometric processing, mesh optimization, and the integration of AI with physical simulation.

个人主页：https://wangzhendong619.github.io

主持人简介：

Dr. Yin Yang is currently an Associate Professor with the Kahlert School of Computing at the University of Utah. Before joining the U, he was a faculty member at Clemson University and University of New Mexico. He received Ph.D. degree of Computer Science from The University of Texas, Dallas in 2013 (the awardee of David Daniel Fellowship Prize). He was a Research/Teaching Assistant at UT Dallas as well as UT Southwestern Medical Center. His research mainly focuses on real-time physics-based computer graphics, animation and simulation with a strong emphasis on interdisciplinarity. He was a Research Intern in Microsoft Research Asia in 2012. He received NSF CRII (2015) and CAREER (2019) awards. 

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