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.

翻译：本文提出ViSTAR，一种增强现实（AR）环境下的虚拟技能训练系统，支持篮球技能的自导式练习，并提供关于平衡、姿势与时机的反馈。基于对篮球运动员与教练的初步调研，本系统致力于解决技能理解、错误识别与动作纠正三大挑战。ViSTAR遵循行为技能训练（BST）框架——包含指导、示范、演练与反馈四个环节。系统通过视觉叠加提示、节奏与时机提示，以及基于三维动作重建的AI教练代理提供反馈。我们通过分析时空关节数据，并利用大型语言模型（LLM）将动作特征映射为自然语言指导提示，从而生成言语反馈。其核心创新在于这种反馈生成机制：运动特征被转化为简洁的教练指导洞见。两项用户研究（共16名参与者）表明，参与者普遍更倾向于本系统生成的AI反馈而非传统教练反馈，并报告ViSTAR有助于他们察觉姿势与平衡问题，实现超越自我观察的动作优化。