## 4 Experiments
### Experimental Results
In particular, Transformer variants exhibit highly consistent convergence and competitive accuracy, suggesting that the augmentation pipeline enables them to fully leverage their representational capacity for gesture modeling. To quantify the effectiveness of the proposed IK-based data augmentation strategy, we conducted controlled experiments by training all five models CNN3D, TCN, Transformer, Informer, and Sparse Transformer with and without augmentation. The results are presented in Figure 6.  
Figure 6 shows the validation accuracy comparison under both settings. For all models, introducing IK-based augmentation consistently leads to higher validation accuracy, confirming the general benefit of the method. Notably, the improvement is most prominent in Transformer-based architectures. The vanilla Transformer sees its validation accuracy increase from 87.1% to 93.2%, while Informer and Sparse Transformer both improve by more than 4.7%. To further highlight the contribution of each model, Figure visualizes the proportion of total accuracy gain attributable to each architecture. The Transformer model accounts for the largest share (0.061), followed closely by Informer (0.058) and Sparse Transformer (0.047). These observations demonstrate that the augmentation strategy not only improves model performance uniformly, but also enables attention-based models to realize their full potential on complex gesture sequences. Together, these results underscore the robustness and scalability of our augmentation pipeline across different model paradigms. To further demonstrate the advantage of our IK-based augmentation strategy, we established a baseline using a conventional augmentation technique, specifically, 2D random rotation. Each model was trained with pose sequences augmented by randomly rotating the hand keypoints within \(\pm\)10\({}^{\circ}\) in the image plane. As shown in Figure 7, the models trained with IK-AUG consistently outperform those trained with random rotation across all five architectures. These results confirm that IK-AUG provides stronger semantic and structural regularization compared to geometric-only augmentation techniques. To further examine the consistency and robustness of IK-based augmentation across different training budgets, we extended the evaluation to include three epoch settings: 500, 1000, and 2000. The results are summarized in Figure 8.  
As shown in Figure 8, all five models demonstrate consistent improvements in validation accuracy after applying the augmentation method, regardless of the number of training epochs. Transformer-based architectures, particularly Informer and Sparse Transformer, not only exhibit high baseline performance but also maintain stable and notable gains at all epoch levels. For example, Informer achieves gains of 0.040, 0.058, and 0.053 across the 500, 1000, and 2000 epoch settings, respectively. To better understand the influence of training duration on augmentation effectiveness, Figure 6 plots the accuracy gains over epochs for each model. Most models show either a stable or upward trend in gain magnitude as training progresses, with Transformer achieving the largest gain at 1000 epochs (+0.070), and Informer sustaining gains above 0.05 throughout. This suggests that our augmentation strategy scales well with increased training and can be fully leveraged by attention-based architectures. Figure 5: Comparative performance of deep learning models on augmented gesture data. (**a**) illustrates the convergence behavior of five architectures across 1000 training epochs, showing consistent learning stability and reduced variance with augmentation. (**b**) presents final training and validation accuracy, where all models achieve high generalization, with Transformer-based models demonstrating a strong balance between training fit and validation performance. Figure 6: \(Cont\). Figure 8 compares the final validation accuracy on the augmented dataset alone, without reference to raw data performance.