The performance of HiLo is sensitive to the quality and accuracy of the estimated parametric body model, as inaccuracies in the body model estimation can impact the reconstruction results. To enhance the robustness of HiLo to large errors in the body model estimation, several improvements can be considered:
Error Handling Mechanisms: Implement mechanisms to detect and handle large errors in the body model estimation, such as outlier rejection or data augmentation techniques to simulate extreme cases.
Adaptive Learning: Develop adaptive learning strategies that adjust the reconstruction process based on the confidence level of the body model estimation, focusing more on high-confidence regions and refining uncertain areas.
Ensemble Methods: Incorporate ensemble methods to combine multiple estimations of the body model and leverage the diversity of predictions to improve overall reconstruction accuracy.
Regularization Techniques: Introduce regularization techniques that penalize deviations from expected body model characteristics, helping the reconstruction process to stay consistent even in the presence of errors.
By implementing these enhancements, HiLo can be further improved to handle large errors in the body model estimation and enhance its robustness in challenging scenarios.