布料仿真一直是计算机动画中的研究热点与难点，对提高计算机动画质量以及用户体验具有重要意义，布料是一种非常经典的柔性材料物体，遍布于人们的日常生活中。虚拟世界中虚拟角色强烈的视觉真实感主要来源于逼真的虚拟人物的服装动画，这在很大程度上可以增强用户的体验感，在游戏娱乐、电影电视和动画制作等领域有着十分广泛的应用前景。布料仿真的质量与速度直接决定了计算机动画的整体水平，而布料的模拟水平则起着至关重要的作用。随着计算机软硬件的不断发展和计算机动画市场需求的提高，对布料仿真建模方法的研究受到越来越多的关注，布料仿真建模方法也因此有了较大发展。本文通过回顾布料仿真建模方法的相关工作，对国内外方法的研究进展进行综述，从布料仿真中数值积分方法的改进、多分辨率网格的改进和机器学习方法的使用等方面对布料仿真方法的发展进行简要阐述，并根据不同方法在布料模拟应用上的特性，对几大类改进方法进行了相应的总结与展望。同时选取几种算法进行对比，并给出建议。

The study of cloth modeling methods has a long history. Cloth simulation has always been a popular and difficult research topic in computer animation. Improving the quality of computer animation and user experience is of great significance. Cloth is a classic flexible material object, which can be seen almost everywhere in people's daily life. The clothing animation of realistic virtual characters can bring a strong sense of visual reality to the virtual characters and can enhance the user experience. It has very broad application prospects in animation production, game entertainment, film and television, and other fields. In addition, this technology can also be applied to the clothing industry, where virtual clothing can be used to design and display clothing more intuitively. In recent years, with the continuous emergence of applications involving virtual reality and human-computer interaction, especially the emergence of network virtual environments with high interactive characteristics, people's demand for high-quality real-time virtual character clothing animation has increased. Efficiently and realistically simulating the movement of cloth (e.g., flags, clothing, tablecloths) on a computer has become a very challenging subject. Cloth animation is an important branch in the field of computer animation, belonging to the category of soft body fabric material deformation animation. In cloth simulation modeling, the accuracy of cloth simulation and the speed of cloth simulation often restrict each other. At present, some traditional cloth simulation methods can only take into account one of the two, and it is difficult to achieve a balance. Therefore, researchers have found a method that can balance the simulation accuracy and simulation speed to a certain degree. It is the focus of research in cloth simulation technology. When performing simulation modeling for flexible fabrics, constructing an appropriate and accurate modeling method has become the key to cloth simulation technology. After years of development of cloth simulation research, there are currently three mainstream cloth modeling methods:geometric modeling-based methods, physics-based modeling methods, and hybrid-based modeling methods. Hybrid-based modeling methods are a combination between geometry-based methods and physics-based methods. They require more calculation time compared with geometric modeling methods and have a lower accuracy compared with physics-based methods. In cloth simulation research, the main problem at present is how to meet the increasing real-time requirements on the basis of ensuring the cloth simulation effect. In response to this problem, researchers have made contributions in different ways, including the continuous development of numerical integration, from explicit Euler integration, implicit Euler integration to fourth-order Runge-Kutta and Verlet integration. The development of numerical integration has reduced the numerical calculation time in cloth simulation to a certain extent. In recent years, algorithms combined with machine learning have emerged in various fields. In computer animation, especially in the field of cloth simulation, researchers have begun to use the idea of machine learning to optimize cloth modeling. The commonly used methods of machine learning are convolutional neural network, recurrent neural network, back propagation(BP) neural network, and random forest. This study reviews the related work of cloth simulation modeling methods and summarizes the research and development of methods in China and abroad. According to the improvement of the cloth integration method, the improvement of the multi-resolution grid, and the use of machine learning methods, the development of the cloth simulation method is briefly described. According to the characteristics of the integral method and the multi-resolution grid method and the characteristics of the application of machine learning methods in cloth simulation, several major types of improved methods are summarized and prospected accordingly. Researchers have some considerations whether to improve the simulation speed or to ensure the speed to improve the simulation accuracy. Because of their different research entry points for the improvement of cloth modeling methods, their research purposes are also different. This article selects several algorithms to make a corresponding comparison and provides some suggestions for learners to learn from.