# RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline
## 6 Conclusions  
This paper summarizes the common ambiguities present in existing pose comparison methods and introduces a new benchmark, PoseAMBench, which is divided into three sets based on the source of ambiguity: the Shape-Variations Set, the Viewpoint-Variations Set, and the Torsional-Pose Set. A robust pose similarity estimation method capable of overcoming these ambiguities should perform well on this benchmark. To address these ambiguities, we propose a RotJoint-based human pose comparison pipeline, leveraging RotJoint parameters for action similarity measurement. On PoseAMBench, our method achieves scores of 92.7 on the Shape-Variations Set, 93.5 on the Viewpoint-Variations Set, and 91.6 on the Torsional-Pose Set, while the best results from 2D and 3D skeleton-based methods on these three sets are 78.0, 84.6, and 77.6, respectively. The experimental results demonstrate that, compared to traditional skeleton-based approaches, our method aligns more closely with human judgments and achieves state-of-the-art performance in distinguishing ambiguous poses. Furthermore, to validate the potential of our current work \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline ST-GCN [6] & 0.82 & 0.80 & 0.83 & 0.81 & 0.90 \\
2s-AGCN [7] & **0.88** & 0.87 & **0.89** & 0.88 & 0.94 \\ CTR-GCN [56] & 0.85 & 0.84 & 0.86 & 0.85 & 0.92 \\ \hline TRNet (Ours) & **0.88** & **0.91** & **0.89** & **0.90** & **0.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance of different base network architectures in open-set action recognition. The bolded values represent the maximum values across all settings. Figure 9: To facilitate visualization, we plotted the confusion matrix for the 24 categories in the test set of the open-set classification task. in extending to multi-frame continuous actions in the temporal dimension, we conduct a preliminary verification by extending the pipeline to the spatiotemporal domain using dynamic time warping. This approach achieves 0.996 similarity for the same individual performing actions at different speeds and 0.909 similarity for different individuals under varying speeds and viewpoints. Our experiments demonstrate the robustness and transferability of the RotJoint-based method for action sequence matching. Additionally, we introduce TemporalRotNet, a Transformer-based network designed for dynamic action comparison. Through the integration of class tokens, it supports tasks such as action classification, recognition, and similarity evaluation, achieving outstanding results, including 93.7% accuracy on closed-set recognition with the NTU RGB+D dataset. In summary, this paper identifies the ambiguities in existing pose comparison methods, proposes a RotJoint-based pose comparison pipeline, and introduces the PoseAM-Bench benchmark. The experimental results demonstrate the superior performance of the proposed method in pose similarity estimation, while its capability in dynamic action comparison is further validated through dynamic time warping and TemporalRotNet. Conceptualization, G.G., R.X. and H.Z. ; Methodology, G.G. ; Software, G.G. ; Validation, G.G. and Z.Z. ; Data curation, G.G., G.Y., Z.L. and Y.Q. ; Writing--original draft, G.G. ; Writing--review and editing, Y.Y. ; Funding acquisition, G.Y. All authors have read and agreed to the published version of the manuscript. This work is supported by the \"Leading Goose + X\" Science and Technology Program of Zhejiang Province of China (2025C02104). The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of College of Biomedical Engineering & Instrument Science, Zhejiang University of [2022]51, approved on 5 December 2022. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data used in this study are not fully open access.