BasketMotion

# Jump3D-Net **Spatial Biomechanics-Driven 3D Motion Transformer for Personalized Jump Shot Optimization in Elite Basketball** --- ## OverviewJump3D-Net is a novel deep learning framework that integrates **spatial biomechanics** with **transformer-based architectures** to optimize jump shot mechanics for elite basketball players. It leverages advanced motion modeling and personalized biomechanical insights to provide accurate performance analysis and actionable optimization strategies. --- ## Key Features- **3D Motion Transformer**: Captures both spatial and temporal dynamics of jump shots. - **Personalized Biomechanical Analysis**: Integrates individual movement data to tailor performance insights. - **Trajectory-Aware Cognitive Embedding Network (TACEN)**: Learns player behavior and decision-making patterns. - **Adaptive Foresight Calibration (AFC)**: Aligns predictions with domain-specific performance metrics and strategic goals. --- ## Datasets- **Basketball Dataset** – Real-world motion capture and tactical execution data. - **MoVi Dataset** – High-fidelity multi-view motion capture for cross-domain motion analysis. - **AMASS Dataset** – Aggregated motion datasets for general body movement analysis. - **SportVU Dataset** – NBA-level spatial tracking data for advanced tactical analytics. --- ## Model Architecture- **Spatial-Temporal Encoding** – Captures dynamic player movement patterns. - **Interaction Graph Reasoning** – Models multi-agent influences and team dynamics. - **Disentangled Behavior Summary** – Produces interpretable latent traits (positioning, action patterns, pressure response). - **Latent-Aware Action Simulation** – Predicts alternative behaviors and strategic adaptations. --- ## PerformanceJump3D-Net outperforms state-of-the-art models like TPNet, TrajectoryFormer, and AgentFormer with: - **Accuracy**: 90–91% across datasets - **AUC**: Up to 92.4% for cross-domain performance - Improved generalization and interpretability compared to prior motion prediction frameworks. --- ## Installation```bashgit clone https://github.com/your-username/Jump3D-Net.gitcd Jump3D-Netpip install -r requirements.txt