Spatiotemporal Attention Generative Adversarial Networks are Used for Real-Time Motion Capture Data Augmentation

In order to solve the problems encountered by traditional methods in data processing and real-time, this paper proposes a new solution based on spatiotemporal attention generative adversarial network model. Based on the comparative analysis in multiple application scenarios, the study proves that the model has achieved significant improvements in motion capture accuracy, training time, and real-time processing ability. Experimental results show that the accuracy of action recognition is increased by 13.5%, the training time is reduced by 37.5%, and the real-time processing capacity is increased by 20% after using the spatiotemporal attention generative adversarial network model. It can be proved that the spatiotemporal attention generative adversarial network can be used for real-time motion capture data augmentation, and effectively promote the application of virtual reality and augmented reality technology in the industrial field, solve the limitations of traditional methods, and effectively consolidate the technological leadership of enterprises in the industry.