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

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