Action recognition based on multimode fusion for VR online platform

The current popular online communication platforms can convey information only in the form of text, voice, pictures, and other electronic means. The richness and reliability of information is not comparable to traditional face-to-face communication. The use of virtual reality (VR) technology for online communication is a viable alternative to face-to-face communication. In the current VR online communication platform, users are in a virtual world in the form of avatars, which can achieve "face-to-face" communication to a certain extent. However, the actions of the avatar do not follow the user, which makes the communication process less realistic. Decision-makers need to make decisions based on the behavior of VR users, but there are no effective methods for action data collection in VR environments. In our work, three modalities of nine actions from VR users are collected using a virtual reality head-mounted display (VR HMD) built-in sensors, RGB cameras and human pose estimation. Using these data and advanced multimodal fusion action recognition networks, we obtained a high accuracy action recognition model. In addition, we take advantage of the VR HMD to collect 3D position data and design a 2D key point augmentation scheme for VR users. Using the augmented 2D key point data and VR HMD sensor data, we can train action recognition models with high accuracy and strong stability. In data collection and experimental work, we focus our research on classroom scenes, and the results can be extended to other scenes.