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：我们提出ViSTAR，一种支持篮球技能自主训练的增强现实系统，可提供关于平衡、姿势和时机的实时反馈。通过篮球运动员和教练的构成性研究，该系统针对技能理解、错误识别与纠偏三大挑战。ViSTAR遵循行为技能训练框架（指令、示范、演练、反馈），通过视觉叠加层、节奏与时机提示，以及基于3D运动重建的AI教练智能体提供反馈。我们通过分析时空关节数据，利用大语言模型将运动特征映射为自然语言教练提示，生成口头反馈。关键创新在于反馈生成流程：运动特征被转化为简洁的教练洞见。在两项研究（N=16）中，参与者普遍更青睐AI生成的反馈而非教练反馈，并报告ViSTAR帮助他们注意到姿势与平衡问题，进而优化超越自我观察范围的动作细节。