Progress of cloth simulation modeling

Abstract

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.