From Sports Videos to Immersive Training: Augmenting Human Motion to Enrich Basketball Training Experience
Yihong Wu, Xiao Xie, Lingyun Yu, Xinyi Ruan, Runzhou Li, Liqi Cheng, Shuainan Ye, Dazhen Deng, Hui Zhang, Yingcai Wu
Abstract
Video plays a crucial role in sports training, enabling participants to analyze their movements and identify opponents’ weaknesses. Despite the easy access to sports videos, the rich motion data within them remains underutilized due to the lack of clear performance indicators and discrepancies from real-game conditions. To address this, we employed advanced computer vision algorithms to reconstruct human motions in an immersive environment, where users can freely observe and interact with the movements. Basketball shooting was chosen as a representative scenario to validate this framework, given its fast pace and extensive physical contact. Collaborating with experts, we iteratively designed motion-related visualizations to improve the understanding of complex movements. A one-on-one matchup simulating real games was also provided, allowing users to compete directly with the reconstructed motions. Our user studies demonstrate that this method enhances participants’ movement comprehension and engagement, while insights derived from interviews inform future immersive training designs.
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