The reliance on the AMASS dataset in WHAM may pose limitations in generalizing to a wider range of human motions, especially those that are not well-represented in the dataset. To address this limitation and improve generalization, WHAM could benefit from incorporating additional diverse datasets that cover a broader spectrum of human motions, poses, and interactions. By training the network on a more extensive and varied dataset, WHAM can learn to capture a wider range of human movements and behaviors, leading to improved accuracy and robustness in real-world scenarios. Furthermore, techniques such as data augmentation, domain adaptation, and transfer learning could be employed to enhance the model's ability to generalize to new and unseen motions.