Neural3Points: Learning to Generate Physically Realistic Full-body Motion for Virtual Reality Users

TL;DR

This paper introduces Neural3Points, a data-driven physics-based system that predicts realistic full-body VR avatar movements from sparse sensor data, enabling natural and real-time virtual interactions.

Contribution

It presents a novel reinforcement learning approach with pretraining for accurate full-body motion prediction from limited VR tracker data.

Findings

Effective real-time full-body motion prediction demonstrated

High realism in avatar movements achieved

System outperforms baseline methods in accuracy

Abstract

Animating an avatar that reflects a user's action in the VR world enables natural interactions with the virtual environment. It has the potential to allow remote users to communicate and collaborate in a way as if they met in person. However, a typical VR system provides only a very sparse set of up to three positional sensors, including a head-mounted display (HMD) and optionally two hand-held controllers, making the estimation of the user's full-body movement a difficult problem. In this work, we present a data-driven physics-based method for predicting the realistic full-body movement of the user according to the transformations of these VR trackers and simulating an avatar character to mimic such user actions in the virtual world in real-time. We train our system using reinforcement learning with carefully designed pretraining processes to ensure the success of the training and the quality of the simulation. We demonstrate the effectiveness of the method with an extensive set of examples.