Peter Zha ¹ and Soroush Mirzaee ², ¹ USA, ² California State Polytechnic University, USA

Tennis players often face barriers to consistent practice due to weather, court availability, and scheduling conflicts [1]. This paper presents an intelligent video game developed with Unity and pose estimation to enable indoor tennis training [2]. Utilizing BlazePose and Unity Sentis, the system tracks player movements via webcam, translating them into a 3D avatar for interactive drills against a virtual ball dispenser [3]. Key challenges included optimizing pose estimation latency, sourcing 3D models, and rendering realistic graphics while keeping the performance high, which was addressed through Sentis integration, Blender tools, and iterative lighting adjustments. Experiments comparing BlazePose, OpenPose, and MoveNet revealed BlazePose's superior latency (28ms) and accuracy over the other two, validating its efficiency with Sentis. This accessible, desktop-based solution outperforms traditional methods by eliminating environmental dependencies and reducing costs. It empowers players to maintain skill development, offering a practical tool for tennis enthusiasts globally.

Sentis, Pose estimation, Unity, Video game, Tennis Training