Human Mesh Reconstruction of Sports Players with Multiple Dynamic Cameras

Accurate human motion estimation is crucial for sports motion analysis. Traditional approaches often depend on motion capture systems or controlled environments, limiting their applicability to real-world sports scenarios. In this work, we propose a novel 3D human motion estimation method that leverages multiple dynamically moving cameras in an uncalibrated setting, eliminating the need for prior environmental knowledge. Our approach reconstructs human meshes from multiple viewpoints and adaptively assigns confidence-based weights to each view, ensuring robust motion estimation even under occlusions and varying camera positions. By integrating confidence-aware multiview fusion, our method significantly improves the accuracy and robustness of 3D human mesh reconstruction in unconstrained sports environments. This work advances the feasibility of motion analysis in real-world settings without requiring specialized motion capture systems or rigid spatial constraints.