Research on Multi-Precision Fabric Modeling Method Based on Machine Learning

Abstract

In order to solve the shortcomings of complex calculation and time consumption of fabric physical simulation methods, many single-precision fabric simulation technologies based on machine learning methods have emerged and the amount of calculation is increased for regions with small changes. Aiming at this problem, this paper proposes two multi-precision fabric modeling method based on machine learning. Firstly, the fabric mesh is simulated by the physical method, the initial position of the vertex is calculated, and the deformation of each region of the fabric is measured by Rayleigh quotient curvature, and the multi-precision fabric mesh is constructed. Secondly, the multi-precision fabric graph structure and geometry image are extracted from the multiprecision fabric mesh. Finally, the subgraph convolutional neural network and super-resolution network are trained to model the multi-precision fabric, and we compared the two different multi-precision fabric machine learning modeling methods. Through the experimental verification, in the garment modeling, the garment modeled by the subgraph convolutional neural network is no longer only dependent on the change of human joints, resulting in a more realistic effect. At the same time, the efficiency of the subgraph convolutional neural network is 25.3% higher than that of the single-precision garment modeling based on the machine learning method. In the cloth simulation, speed of the super-resolution network is nearly 16 times faster than that of the physical simulation, which supplements the imperfection of insufficient flexibility of the subgraph convolutional neural network modeling.