ViSTAR: Virtual Skill Training with Augmented Reality with 3D Avatars and LLM coaching agent
Chunggi Lee, Hayato Saiki, Tica Lin, Eiji Ikeda, Kenji Suzuki, Chen Zhu-Tian, Hanspeter Pfister
Abstract
We present ViSTAR, a Virtual Skill Training system in AR that supports self-guided basketball skill practice, with feedback on balance, posture, and timing. From a formative study with basketball players and coaches, the system addresses three challenges: understanding skills, identifying errors, and correcting mistakes. ViSTAR follows the Behavioral Skills Training (BST) framework-instruction, modeling, rehearsal, and feedback. It provides feedback through visual overlays, rhythm and timing cues, and an AI-powered coaching agent using 3D motion reconstruction. We generate verbal feedback by analyzing spatio-temporal joint data and mapping features to natural-language coaching cues via a Large Language Model (LLM). A key novelty is this feedback generation: motion features become concise coaching insights. In two studies (N=16), participants generally preferred our AI-generated feedback to coach feedback and reported that ViSTAR helped them notice posture and balance issues and refine movements beyond self-observation.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext b4c163c5-fd71-414e-9455-2b2eb0d57743Builds on13
- Time-LLM: Time Series Forecasting by Reprogramming Large Language ModelsMing Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu et al.ICLR 2024 · 915 citations
- MIRIA: A Mixed Reality Toolkit for the In-Situ Visualization and Analysis of Spatio-Temporal Interaction DataWolfgang Büschel, Anke Lehmann, Raimund DachseltCHI 2021 · 116 citations
- ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User StudiesSebastian Hubenschmid, Jonathan Wieland, Daniel Immanuel Fink, Andrea Batch et al.CHI 2022 · 99 citations
- GPTCoach: Towards LLM-Based Physical Activity CoachingMatthew Jörke, Shardul Sapkota, Lyndsea Warkenthien, Niklas Vainio et al.CHI 2025 · 89 citations
- Towards an Understanding of Situated AR Visualization for Basketball Free-Throw TrainingTica Lin, Rishi Singh, Yalong Yang, Carolina Nobre et al.CHI 2021 · 81 citations
Related papers
- AgentCoach: LLM-Based Adaptive Coaching Feedback for Motor Skill LearningDizhi Ma, Jiakun Yu, Xinyi Wang, Xiyun Hu et al.CHI 2026 · 1 citation
- SoleCoach: Sole Pressure and IMU-based MLLMs for Skill CoachingToshihiro Hirano, Hitoshi Yoshihara, Yichen Peng, Chen-Chieh Liao et al.CHI 2026 · 1 citation
- ExpertAF: Expert Actionable Feedback from VideoKumar Ashutosh, Tushar Nagarajan, Georgios Pavlakos, Kris Kitani et al.CVPR 2025
- Vistar: Enhancing the Perception Capability of LLMs under Imprecise IMU-Text AlignmentYatong Chen, Chenzhi Hu, Bowen He, Ruijie Wang et al.KDD 2026
- Bridging Coaching Knowledge and AI Feedback to Enhance Motor Learning in Basketball Shooting Mechanics Through a Knowledge-Based SOP FrameworkJian-Jia Weng, Calvin Ku, Jo-Chien Wang, Chih-Jen Cheng et al.CHI 2025 · 10 citations
