From Sports Videos to Immersive Training: Augmenting Human Motion to Enrich Basketball Training Experience
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.
Main reference:
Yihong Wu, Xiao Xie, Lingyun Yu, Xinyi Ruan, Runzhou Li, Liqi Cheng, Shuainan Ye, Dazhen Deng, Hui Zhang, and Yingcai Wu. 2025. From Sports Videos to Immersive Training: Augmenting Human Motion to Enrich Basketball Training Experience. In Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25). Association for Computing Machinery, New York, NY, USA, Article 116, 1–17. https://doi.org/10.1145/3746059.3747762
BibTeX:
@inproceedings{10.1145/3746059.3747762,
author = {Wu, Yihong and Xie, Xiao and Yu, Lingyun and Ruan, Xinyi and Li, Runzhou and Cheng, Liqi and Ye, Shuainan and Deng, Dazhen and Zhang, Hui and Wu, Yingcai},
title = {From Sports Videos to Immersive Training: Augmenting Human Motion to Enrich Basketball Training Experience},
year = {2025},
isbn = {9798400720376},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3746059.3747762},
doi = {10.1145/3746059.3747762},
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.},
booktitle = {Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology},
articleno = {116},
numpages = {17},
keywords = {SportsXR, Immersive Training, Mixed Reality},
location = {
},
series = {UIST '25}
}