Work

avaTTAR: Table Tennis Stroke Training with Embodied and Detached Visualization in Augmented Reality

Year: 2024

Type: conference-paper

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.

Source: ACM Symposium on User Interface Software and Technology (UIST)

DOI: 10.1145/3654777.3676400

Authors: Dizhi Ma, Xiyun Hu, Jingyu Shi, Mayank Patel, Rahul Jain, Ziyi Liu, Zhengzhe Zhu, Karthik Ramani

Institution: Purdue University West Lafayette

Language: English

Topic: Augmented Reality Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Purdue University

OpenAlex ID: W4403333826

FWCI: 4.431

Cites: 56

Cited by: 21

Related to: 10