VisMimic: Integrating Motion Chain in Feedback Video Generation for Motor Coaching
Liqi Cheng, Xiao Xie, Yiwei Peng, Minghao Feng, Yuchen He, Anqi Cao, Yihong Wu, Hui Zhang, Yingcai Wu
Abstract
Augmented video is a common medium for remote sports coaching, facilitating communication between trainees and coaches. Existing video augmentation techniques struggle to simultaneously convey both the overall motion dynamics and static key poses. This limitation hinders feedback comprehension in motor learning, making it difficult to understand where errors occur and how to correct them. To address this, we first reviewed popular video augmentation solutions. In collaboration with professional coaches, we integrated motion chain into feedback videos to combine key poses with motion trajectories. It supports multi-view observation and feedback explanation from overview to detail. To assist coaches in creating feedback videos, we present VisMimic, a human-AI interaction system that automatically analyzes trainee videos against reference movements, generates animated feedback, and enables customization. User studies show VisMimic’s usability and effectiveness in enhancing motion analysis and communication for motor coaching.
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