ViSTAR: Virtual Skill Training with Augmented Reality with 3D Avatars and LLM coaching agent
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911524063281152 |
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| author | Lee, Chunggi Saiki, Hayato Lin, Tica Ikeda, Eiji Suzuki, Kenji Zhu-Tian, Chen Pfister, Hanspeter |
| author_facet | Lee, Chunggi Saiki, Hayato Lin, Tica Ikeda, Eiji Suzuki, Kenji Zhu-Tian, Chen Pfister, Hanspeter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22077 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ViSTAR: Virtual Skill Training with Augmented Reality with 3D Avatars and LLM coaching agent Lee, Chunggi Saiki, Hayato Lin, Tica Ikeda, Eiji Suzuki, Kenji Zhu-Tian, Chen Pfister, Hanspeter Human-Computer Interaction 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. |
| title | ViSTAR: Virtual Skill Training with Augmented Reality with 3D Avatars and LLM coaching agent |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.22077 |