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纸质出版日期： 2024-01-16 ，
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任靖雯， 戴俊飞， 蔺宏伟. 2024. 等几何连续介质弹性模型的带厚度布料仿真方法. 中国图象图形学报， 29(01):0243-0255
Ren Jingwen， Dai Junfei， Lin Hongwei. 2024. Simulation of cloth with thickness based on isogeometric continuum elastic model. Journal of Image and Graphics， 29(01):0243-0255
目的
2
布料仿真是计算机动画领域的一个研究热点和难点，基于粒子系统的模型快速高效却难以准确描述布料真实的物理特性，这些物理属性可以由连续介质的弹性模型利用有限元方法来实现，但所需网格单元较多，求解复杂且耗时。现有方法通常将布料建模为曲面和壳模型，对较厚布料的仿真能力较弱。针对这些常用模型在几何建模、物理真实性和仿真速度上难以兼顾的问题，提出了一种带厚度的布料仿真模型，能够更真实地表达不同厚度布料的形变行为，并利用等几何分析方法进行基于物理的快速动态仿真模拟。
方法
2
将有厚度的布料建模为三变量B样条体表示的薄板模型，利用等几何—伽辽金方法，推导出在给定边界条件下三维连续介质线弹性力学方程的弱形式，将B样条体的控制网格作为计算网格进行仿真分析，最后在隐式动力系统框架下对线性方程组直接求解。
结果
2
对本文布料模型进行了多方面的讨论和分析。首先，与几种主流的离散模型下的模拟效果进行了光滑度的对比，本文方法的NURBS（nonuniform rational B-splines）建模有着明显的光滑性优势；并在不同自由度下比较了与经典有限元连续介质模型的计算时间，结果表明模拟结果的平方根误差（root mean squared error，RMSE）小于0.04时，本文方法至多能减少90.23%的自由度和99.43%的计算时间；与同厚度面料的连续介质壳模型相比，计算时间减少约30%。其次，对于经典场景如悬布、旗帜和接触问题，实现了逼真快速的动态模拟效果。此外，还展示和讨论了控制网格的密度、基函数的阶数和物理参数等的选择对模拟效果的影响，验证了通过适当的几何参数和物理参数，使用更高分辨率的控制网格或更高阶的基函数将会促进更多的模拟细节效果。
结论
2
本文提出的等几何方法模拟的厚布料模型是同时满足仿真效果和速度基本要求的有效方法，并且样条基能保持布料的光滑度，实现了更高的动态模拟效率。
Objective
2
Cloth simulation is a research hotspot and difficulty in the field of computer animation. Cloth simulation can be seen in a variety of topics such as visual effects， game development， industrial design， and interactive virtual environments. With the demand for high-quality experience from users today， various models have been proposed to improve simulation performance. Although the models based on the particle system are fast and efficient， they have difficulty accurately capturing the behaviors in accordance with the real physical properties of cloth. These physical properties can be described by the elastic model of continuum employing finite element method （FEM）. However， solving with FEM in cloth simulation requires a number of degrees of freedom （or elements）， and it is much more complex and timeconsuming. Therefore， existing methods usually model cloth as a surface or a shell， which leads to weak simulation ability of thick cloth. To ease the awkwardness of compromising the geometric modeling， physical authenticity， and computation speed in these models， a new cloth simulation model with thickness is proposed， which describes the deformation behavior of the cloth with different thicknesses more appropriately， and a fast dynamic physically based cloth simulation algorithm is carried out by isogeometric analysis （IGA）. IGA treats the physical domain （the geometry） as the computational field， avoiding the mesh generation that has approximating error and is timeconsuming in classical FEM. IGA uses the nonuniform rational B-splines （NURBS） basis functions for the physical domain and the solution field， which has the merit of higher-order continuous solution compared with the traditional linear basis. The direct computation on the control mesh of the physical domain makes solving the physical problems more accurate and faster.
Method
2
The thick cloth is initially modeled as a very thin plate expressed by a trivariate B-spline solid. The weft direction and the warp direction of the fabric are free to design， while the basis for the thickness direction is usually linear to decrease the degrees of freedom， or higher order for thicker cloth. The deformations of the B-spline solid with elasticity represent the behaviors of displacement of the cloth. Focused on IGA-Galerkin method， the weak form of the linear elastic equations of 3D continuum is derived under the given boundary conditions. Then， the integrals in the weak form are computed by Gauss quadrature. By assembling the global stiffness matrix from the local element matrices， a linear system is yielded. The Dirichlet boundary conditions are dealt with Gauss elimination， and the preprocessing of the matching between the index of the local basis and the index of the global basis is also needed. The control mesh is simulated and analyzed as the computing mesh， so the unknowns in the linear system are the control coefficients in the B-splines-expressed solution of the displacement. The damping behavior caused by the dissipation of the energy of the system is modeled as the damping coefficient to the velocity of the control points to simplify the simulation. Considering the dynamic process， the time integration is realized by the Newmark implicit method to allow a larger time step and enhance the stability of the system. Finally， the linear equations are solved directly due to the less degrees of freedom compared with other models， and the displacements， velocities， and accelerations of the control points of the cloth are updated for each time step. The current state of the cloth can be visualized through the current positions of the control points.
Result
2
Our IGA continuum elastic cloth model is discussed in various aspects. First， the smoothness of the simulation results is compared with commonly used discrete models， which displays remarkable smoothness advantage due to NURBS construction， and the computational time is compared with the classical finite element continuum model at different degrees of freedom， which shows that when the root mean squared error （RMSE） of the simulation results of the two models is less than 0.04， the method can reduce at most 90.23% of the degrees of freedom and 99.43% of the computational time. Compared with the continuum shell-based model of the same thickness， the computational time can be improved by about 30%. Second， for classic scenarios such as hanging cloth， falling flag， and contact problems， realistic， fast dynamic simulation effects are achieved. In addition， the influences of the density of the control mesh， the order of the basis function， and the selection of physical parameters on the simulation effect are demonstrated and discussed. Using higher-resolution control mesh or higher-order basis with appropriate geometric and physical parameters promotes more detailed simulation effects.
Conclusion
2
In this paper， an IGA-Galerkin-based cloth model with thickness is proposed to improve the physically based simulation， which is very intuitive and easy to implement. The trivariate B-splines-expressed model of a very thin plate can keep the smoothness of the cloth and uses less degrees of freedom and elements. The focus on solving the elastic equilibrium equations of the continuum enables matching the simulated cloth with the fabrics in the real world. The proposed IGA-Galerkin cloth model is an effective approach to meet the basic requirements of simulation accuracy and speed， which achieves a higher dynamic physical simulation efficiency.
等几何分析（IGA）有限元方法（FEM）弹性力学物理仿真布料仿真
isogeometric analysis （IGA）finite element method （FEM）elastic mechanicsphysically based simulationcloth simulation
Adikari S B， Ganegoda N C， Meegama R G N and Wanniarachchi I L. 2020. Applicability of a single depth sensor in real-time 3D clothes simulation： augmented reality virtual dressing room using kinect sensor. Advances in Human-Computer Interaction， 2020： #1314598 ［DOI： 10.1155/2020/1314598http://dx.doi.org/10.1155/2020/1314598］
Bouaziz S， Martin S， Liu T T， Kavan L and Pauly M. 2014. Projective dynamics： fusing constraint projections for fast simulation. ACM Transactions on Graphics， 33（4）： #154 ［DOI： 10.1145/2601097.2601116http://dx.doi.org/10.1145/2601097.2601116］
Chen L， Ye J T and Zhang X P. 2021. Multi-feature super-resolution network for cloth wrinkle synthesis. Journal of Computer Science and Technology， 36（3）： 478-493 ［DOI： 10.1007/s11390-021-1331-yhttp://dx.doi.org/10.1007/s11390-021-1331-y］
Clegg A， Erickson Z， Grady P， Turk G， Kemp C C and Liu C K. 2020. Learning to collaborate from simulation for robot-assisted dressing. IEEE Robotics and Automation Letters， 5（2）： 2746-2753 ［DOI： 10.1109/LRA.2020.2972852http://dx.doi.org/10.1109/LRA.2020.2972852］
Hughes T J R， Cottrell J A and Bazilevs Y. 2005. Isogeometric analysis： CAD， finite elements， NURBS， exact geometry and mesh refinement. Computer Methods in Applied Mechanics and Engineering， 194（39/41）： 4135-4195 ［DOI： 10.1016/j.cma.2004.10.008http://dx.doi.org/10.1016/j.cma.2004.10.008］
Jiang J W， Sheng B， Li P， Ma L Z， Tong X and Wu E H. 2020. Real-time hair simulation with heptadiagonal decomposition on mass spring system. Graphical Models， 111： #101077 ［DOI： 10.1016/j.gmod.2020.101077http://dx.doi.org/10.1016/j.gmod.2020.101077］
Jin Y X， Ma B， Jia Y， Chen Z X and Lu Y. 2022. Neural network fusion based cloth collision detection algorithm. Journal of Image and Graphics， 27（7）： 2251-2262
靳雁霞， 马博， 贾瑶， 陈治旭， 芦烨. 2022. 融合神经网络的布料碰撞检测算法. 中国图象图形学报， 27（7）： 2251-2262 ［DOI： 10.11834/jig.210018http://dx.doi.org/10.11834/jig.210018］
Jin Y X， Zhang J R， Jia Y and Ma B. 2021. Progress of cloth simulation modeling. Journal of Image and Graphics， 26（5）： 970-977
靳雁霞， 张晋瑞， 贾瑶， 马博. 2021. 布料仿真建模研究进展. 中国图象图形学报， 26（5）： 970-977 ［DOI： 10.11834/jig.200216http://dx.doi.org/10.11834/jig.200216］
Kim T. 2020. A finite element formulation of Baraff-Witkin cloth. Computer Graphics Forum， 39（8）： 171-179 ［DOI： 10.1111/cgf.14111http://dx.doi.org/10.1111/cgf.14111］
Li C， Tang M， Tong R F， Cai M， Zhao J Y and Manocha D. 2020. P-cloth： interactive complex cloth simulation on multi-GPU systems using dynamic matrix assembly and pipelined implicit integrators. ACM Transactions on Graphics， 39（6）： #180 ［DOI： 10.1145/3414685.3417763http://dx.doi.org/10.1145/3414685.3417763］
Li P G， Xu G， Ling R， Xiao Z F， Xu J L and Wu Q. 2019. Fabric dynamic simulation by isogeometric mass-spring model. Journal of Computer-Aided Design and Computer Graphics， 31（6）： 911-918
李鹏高， 徐岗， 凌然， 肖周芳， 许金兰， 吴卿. 2019. 采用等几何质点—弹簧模型的布料动态仿真方法. 计算机辅助设计与图形学学报， 31（6）： 911-918 ［DOI： 10.3724/SP.J.1089.2019.17407http://dx.doi.org/10.3724/SP.J.1089.2019.17407］
Li Y F， Du T， Wu K， Xu J and Matusik W. 2022. DiffCloth： differentiable cloth simulation with dry frictional contact. ACM Transactions on Graphics， 42（1）： #2 ［DOI： 10.1145/3527660http://dx.doi.org/10.1145/3527660］
Liu T T， Bargteil A W， O'Brien J F and Kavan L. 2013. Fast simulation of mass-spring systems. ACM Transactions on Graphics， 32（6）： #214 ［DOI： 10.1145/2508363.2508406http://dx.doi.org/10.1145/2508363.2508406］
Liu T T， Bouaziz S and Kavan L. 2017. Quasi-newton methods for real-time simulation of hyperelastic materials. ACM Transactions on Graphics， 36（3）： #23 ［DOI： 10.1145/2990496http://dx.doi.org/10.1145/2990496］
Lu J and Zheng C. 2014. Dynamic cloth simulation by isogeometric analysis. Computer Methods in Applied Mechanics and Engineering， 268： 475-493 ［DOI： 10.1016/j.cma.2013.09.016http://dx.doi.org/10.1016/j.cma.2013.09.016］
Müller M， Heidelberger B， Hennix M and Ratcliff J. 2007. Position based dynamics. Journal of Visual Communication and Image Representation， 18（2）： 109-118 ［DOI： 10.1016/j.jvcir.2007.01.005http://dx.doi.org/10.1016/j.jvcir.2007.01.005］
Overby M， Brown G E， Li J and Narain R. 2017. Admm projective dynamics： fast simulation of hyperelastic models with dynamic constraints. IEEE Transactions on Visualization and Computer Graphics， 23（10）： 2222-2234 ［DOI： 10.1109/TVCG.2017.2730875http://dx.doi.org/10.1109/TVCG.2017.2730875］
Pall P， Fratarcangeli M and Nylèn O. 2018. Fast quadrangular mass-spring systems using red-black ordering//Proceedings of the 14th Workshop on Virtual Reality Interactions and Physical Simulations. Delft， the Netherlands： Eurographics Association： 37-43
Peng X and Zheng C. 2023. An isogeometric cloth simulation based on fast projection method. Computer Modeling in Engineering and Sciences， 134（3）： 1837-1853 ［DOI： 10.32604/cmes.2022.022367http://dx.doi.org/10.32604/cmes.2022.022367］
Piegl L and Tiller W. 1997. The NURBS Book. 2nd ed. Berlin， Heidelberg： Springer ［DOI： 10.1007/978-3-642-59223-2http://dx.doi.org/10.1007/978-3-642-59223-2］
Provot X. 1995. Deformation constraints in a mass-spring model to describe rigid cloth behavior//Proceedings of Graphics Interface Québec， Canada： ［s.n.］： 147-154
Shin S G and Lee C O. 2020. Splitting basis techniques in cloth simulation by isogeometric analysis. Computer Methods in Applied Mechanics and Engineering， 362： #112871 ［DOI： 10.1016/j.cma.2020.112871http://dx.doi.org/10.1016/j.cma.2020.112871］
Tang Y S， Liu S， Deng Y R， Zhang Y H， Yin L R and Zheng W F. 2021. An improved method for soft tissue modeling. Biomedical Signal Processing and Control， 65： #102367 ［DOI： 10.1016/j.bspc.2020.102367http://dx.doi.org/10.1016/j.bspc.2020.102367］
Terzopoulos D， Platt J， Barr A and Fleischer K. 1987. Elastically deformable models//Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques. Anaheim， USA： ACM： 205-214 ［DOI： 10.1145/37401.37427http://dx.doi.org/10.1145/37401.37427］
Volino P， Magnenat-Thalmann N and Faure F. 2009. A simple approach to nonlinear tensile stiffness for accurate cloth simulation. ACM Transactions on Graphics， 28（4）： #105 ［DOI： 10.1145/1559755.1559762http://dx.doi.org/10.1145/1559755.1559762］
Wang H J， Ding Y J， Yang Q Q and Pu H B. 2020. Cloth simulation algorithm based on the mass-spring model and the non-planar vortex lattice model//Frontier Computing. Singapore， Singapore： Springer： 578-585 ［DOI： 10.1007/978-981-15-3250-4_72http://dx.doi.org/10.1007/978-981-15-3250-4_72］
Wang H M. 2015. A chebyshev semi-iterative approach for accelerating projective and position-based dynamics. ACM Transactions on Graphics， 34（6）： #246 ［DOI： 10.1145/2816795.2818063http://dx.doi.org/10.1145/2816795.2818063］
Wang H M and Yang Y. 2016. Descent methods for elastic body simulation on the GPU. ACM Transactions on Graphics， 35（6）： #212 ［DOI： 10.1145/2980179.2980236http://dx.doi.org/10.1145/2980179.2980236］
Xu Z L. 2018. Concise Course in Elasticity. 5th ed. Beijing： Higher Education Press
徐芝纶. 2018. 弹性力学简明教程.5版. 北京： 高等教育出版社
Zhang Z. 2020. Soft-body simulation with CUDA based on mass-spring model and Verlet integration scheme//Proceedings of the ASME 2020 International Mechanical Engineering Conference and Exposition. Online： ASME ［DOI： 10.1115/IMECE2020-23221http://dx.doi.org/10.1115/IMECE2020-23221］
Zienkiewicz O C， Taylor R L and Zhu J Z. 2013. The Finite Element Method： Its Basis and Fundamentals. 7th ed. Oxford： Butterworth-Heinemann ［DOI： 10.1016/C2009-0-24909-9http://dx.doi.org/10.1016/C2009-0-24909-9］
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