Hybrid Neural Network and Physics Engine for Real-time 3D Cloth Simulation

Abstract

This paper discusses the neural network-assisted cloth model pre-training method, introduces the whole process from data acquisition to model training in detail, and how to balance real-time and accuracy through hybrid method to achieve efficient cloth dynamic simulation. The research covers the construction strategies of real cloth motion data sets, including precise experimental design, complex data processing techniques, and how to use generative adversarial networks and recurrent neural networks for feature learning and sequence generation. Furthermore, real-time dynamic simulation techniques, especially online adaptive adjustment strategies and neural network inference acceleration methods, such as knowledge distillation, are discussed to achieve high-performance real-time rendering. Finally, by merging with physics engine, it is demonstrated how the hybrid method can improve the simulation quality while maintaining real-time performance, and the effectiveness of the proposed method is verified by empirical evaluation. Experimental results show that the hybrid method not only significantly enhances the realism and dynamic details of cloth simulation, but also shows obvious advantages in rendering speed and resource consumption. Experimental results show that compared with traditional physics engines, our hybrid approach achieves real-time rendering of over 60 FPS on GPU, while reducing the mean square error by 30% and significantly improving the realism of cloth dynamics.