Digital person training method and system, and digital person driving system

Abstract

This application provides a digital person training method and system, and a digital person driving system. According to the method, human-body pose estimation data in training data is extracted, and the human-body pose estimation data is input into an optimized pose estimation network to obtain human-body pose optimization data. Generation losses of position optimization data and acceleration optimization data in the human-body pose optimization data are calculated based on a loss function of the optimized pose estimation network, so as to minimize errors between position estimation data and acceleration estimation data and a real value. In this way, the optimized pose estimation network is driven to update a network parameter to obtain an optimal driving model that is based on the optimized pose estimation network. The errors between the position estimation data and the acceleration estimation data and the real value are minimized.