avaTTAR: Table Tennis Stroke Training with On-body and Detached Visualization in Augmented Reality
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jul 2024.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025Egocentric Event-Based Vision for Ping Pong Ball Trajectory Predictioncited by 3
- 2025PACE: Physics Augmentation for Coordinated End-to-end Reinforcement Learning toward Versatile Humanoid Table Tenniscited by 2
- 2026BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatchcited by 0
- 2026Generative Tutorial: Towards Live Contextualized Visual Instructions for Physical Taskscited by 0
- 2026Generative Tutorial: Towards Live Contextualized Visual Instructions for Physical Tasks21 Sep 2026
- 2026BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatch23 Mar 2026
- 2025PACE: Physics Augmentation for Coordinated End-to-end Reinforcement Learning toward Versatile Humanoid Table Tennis25 Sep 2025
- 2025Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction9 Jun 2025