# RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline  
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1 College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China; gguo6 zju.edu.cn (G.G. ); [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Y.Q.) 2  
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hether two poses are identical or distinct [5]. The conventional frameworks for action recognition and classification predominantly utilize skeletal data for prediction, including models such as ST-GCN [6], 2s-AGCN [7], MST-GCN [8], and Skeletal-GNN [9]. However, this method is evidently limited in granularity, as it merely offers a binary judgment rather than a detailed comparison [10]. Moreover, this approach is constrained by the finite set of actions in the training data, making it necessary to substantially modify both the dataset and model architecture to recognize new, unseen actions [11], making zero-shot action classification [12; 13] particularly challenging. This limitation significantly impedes the straightforward customization of a standard action library [14; 15]. The second approach focuses on the computation of similarity metrics based on extracted pose features [4; 16]. These methods refer to articulated skeletal points capable of movement as joint nodes, such as shoulder and knee joints, and regard them as key pose representation features. During the pose estimation stage, these methods first predict the positional parameters of these joint nodes, which serve as the foundation for subsequent similarity computations. Traditional methods in this category predominantly rely on 2D and 3D skeleton-based models [17; 18; 19], where comparisons are made by calculating either the angular differences between corresponding joint vectors or the Euclidean distances between corresponding joint nodes [20; 21; 22; 23]. However, as depicted in Figure 1, these methods are often hampered by variations in human body morphology and camera angles, which can alter both the angles between joint vectors and the spatial positions of joint nodes. Furthermore, the skeletal model's inherent limitations in representing the full complexity of human poses can lead to significant ambiguities in the comparison process. To evaluate the performance of existing methods in addressing ambiguities caused by shape variations, viewpoint variations, and torsional poses, with a particular focus on the robustness of action similarity measurement techniques, we develop PoseAMBench. This benchmark is organized into three categories, each designed to assess whether models can maintain strong discriminative capabilities when faced with significant ambiguities. In response to these challenges, we propose a novel protocol for pose comparison that aims to enhance both **generalization** and **robustness**[24]. To enable fine-grained generalization in our model, we developed a similarity evaluation method based on the conceptual framework of the second approach, utilizing pose features and incorporating human perceptual judgments of actions. To enhance the robustness of this method when confronting ambiguities, we introduce rotational parameters known as RotJoints, representing the rotations of joints. As a result, the similarity evaluation can benefit from RotJoint-based feature extraction implementations, effectively addressing ambiguities caused by variations in body shape, multiple viewpoints, and limb rotations. Figure 1: Flowchart outlining the process of this study. In our method, RotJoints are derived from a 3D mesh-based evaluation model; they not only represent the skeletal orientation but also guide the twisting of joint nodes and represent muscle rotations, or in other words, restore muscle rotations. And human actions are defined based on the rotations of joint nodes. This definition provides higher interpretability and aligns more closely with human understanding in the task of pose similarity measurement.