The regression-based inverse-kinematic approach, while effective in predicting pose and shape parameters, may have certain limitations that can be addressed in future work:
Overfitting: Regression models are susceptible to overfitting, especially when trained on limited or biased datasets. To mitigate this, incorporating regularization techniques like dropout or weight decay can help prevent overfitting and improve generalization.
Complexity of Motion: Regression models may struggle with capturing complex and intricate motions, leading to inaccuracies in pose estimation. Introducing more sophisticated network architectures or incorporating hierarchical structures can help the model better represent the nuances of human motion.
Handling Occlusions: Regression models may have difficulty handling occlusions or missing data in the input, which can impact the accuracy of pose estimation. Implementing techniques like data augmentation with occlusions or integrating occlusion-aware modules can improve the model's robustness to incomplete input data.
Real-time Processing: Regression models may have limitations in real-time processing due to computational complexity. Optimizing the model architecture for efficiency and exploring parallel processing techniques can help enhance the model's speed and scalability for real-time applications.
Generalization to Unseen Data: Regression models trained on specific datasets may lack generalization to unseen or diverse data distributions. To address this, incorporating transfer learning from a wider range of datasets or domain adaptation techniques can improve the model's ability to handle diverse scenarios.