avaTTAR: Table Tennis Stroke Training with On-body and Detached Visualization in Augmented Reality

TL;DR

avaTTAR is an augmented reality system that enhances table tennis training by providing dual visual perspectives and real-time feedback through pose estimation, improving player experience and skill development.

Contribution

We introduce avaTTAR, a novel AR training system combining on-body and detached views with pose estimation for effective stroke correction.

Findings

Enhanced training effectiveness demonstrated in user study

Real-time 3D pose reconstruction accuracy confirmed

Dual perspective visualization improves user engagement

Abstract

Table tennis stroke training is a critical aspect of player development. We designed a new augmented reality (AR) system, avaTTAR, for table tennis stroke training. The system provides both "on-body" (first-person view) and "detached" (third-person view) visual cues, enabling users to visualize target strokes and correct their attempts effectively with this dual perspectives setup. By employing a combination of pose estimation algorithms and IMU sensors, avaTTAR captures and reconstructs the 3D body pose and paddle orientation of users during practice, allowing real-time comparison with expert strokes. Through a user study, we affirm avaTTAR's capacity to amplify player experience and training results.