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

arXiv: https://arxiv.org/abs/2407.15373 · HTML: https://mlsanity.com/paper/2407.15373/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction](https://mlsanity.com/paper/2506.07860/) (2025)
- [PACE: Physics Augmentation for Coordinated End-to-end Reinforcement Learning toward Versatile Humanoid Table Tennis](https://mlsanity.com/paper/2509.21690/) (2025)
- [BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatch](https://mlsanity.com/paper/2603.21825/) (2026)
- [Generative Tutorial: Towards Live Contextualized Visual Instructions for Physical Tasks](https://mlsanity.com/paper/2609.24955/) (2026)

## 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.