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arXiv · 2407.15373

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

Dizhi Ma·Xiyun Hu·Jingyu Shi·Mayank Patel·Rahul Jain·Ziyi Liu·Zhengzhe Zhu·Karthik Ramani
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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.

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Dizhi Ma, Xiyun Hu, Jingyu Shi, Mayank Patel, Rahul Jain, Ziyi Liu, Zhengzhe Zhu, Karthik Ramani. 2024-07-22. avaTTAR: Table Tennis Stroke Training with On-body and Detached Visualization in Augmented Reality. https://doi.org/10.1145/3654777.3676400

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