## 4 Experiments
### Ablation Study  
**Effectiveness of interval-frequency descriptor.** The original 3D point cloud data only contain the location information of the points in the 3D space. Even if the time dimension is introduced into the point cloud sequences, it is still challenging to describe the overall spatiotemporal dynamics of the point by relying only on these clues. We have designed an interval-frequency descriptor that captures the onset time and the number of voxel occupancy. This additional information helps us to comprehensively describe human behavior by capturing additional motion features. We conducted ablation studies on the NTU RGB+D 60 dataset where we removed motion feature information in two streams, and the point sets input to SFCNet only had 3D coordinates \((x,y,z)\). The comparison results are presented in Table 3. We observed that without additional three-dimensional features, there was a significant performance degradation of SFCNet by more than 10%.  
This indicates that the interval-frequency descriptor effectively represents the dynamic features within the whole action process, which plays a vital role in 3D action recognition.  
**Effectiveness of two-stream feature fusion.** Different human actions contain different global and local dynamics, we describe the overall motion pattern of the whole human body during the action through the global coarse stream. In contrast, the local fine stream describes the dynamics of crucial body parts, which pays more attention to the details and local features of actions and helps capture the subtle changes and complex action patterns. The results in Table 4 show that our proposed SFCNet fusing the fine-coarse features of the two streams can understand and recognize human actions more comprehensively. First, we discuss the recognition performance in the single-stream state. It can be seen that the local stream has a higher ability to represent the action than the global stream, indicating that it is essential to pay attention to the main parts involved to remove redundancy. In addition, we compare three different feature fusion strategies to demonstrate the superiority of attention-based feature fusion proposed in SFCNet (see Equation (6)). As shown in Table 4, SFCNet (fusion) has apparent advantages over the native cascade or additive fusion strategies. The main reason is that the attention-based feature fusion can adaptively allocate the model's attention to the features from the global coarse stream and the local fine stream. As shown in Figure 5, drinking water only involves hand and head interaction, and the movement amplitude is small, so the model emphasizes the characteristics of the local stream to capture fine-grained movement patterns. On the other hand, punching involves the interaction of two people, and the punching movement is large and powerful, so more attention is paid to the global body appearance while emphasizing some details of the hands and the head. This action-specific feature extraction mechanism improves the generalization ability of SFCNet and accuracy for 3D action recognition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Value of the Threshold \(\theta\)** & **Cross-Subject** & **Cross-View** \\ \hline
15 & 79.5\% & 85.1\% \\
20 & 83.5\% & 93.2\% \\
25 & 86.5\% & 94.4\% \\
30 & **89.9\%** & 96.7\% \\
35 & 87.3\% & 94.9\% \\
40 & 86.9\% & 93.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance on the NTU RGB+D 60 dataset with various values of \(\theta\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Point Feature** & **Cross-Subject** & **Cross-View** \\ \hline (x, y, z) & 78.0\% & 82.3\% \\ (x, y, z, o, f, l) & **89.9\%** & **96.7\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effectiveness of interval-frequency descriptor on the NTU RGB+D 60.