  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.
