Abstract
Human-centric anomaly detection (AD) has been primarily studied to specify anomalous behaviors in a single person. However, as humans by nature tend to act in a collaborative manner, behavioral anomalies can also arise from human-human interactions. Detecting such anomalies using existing single-person AD models is prone to low accuracy, as these approaches are typically not designed to capture the complex and asymmetric dynamics of interactions. In this paper, we introduce a novel task, Human-Human Interaction Anomaly Detection (H2IAD), which aims to identify anomalous interactive behaviors within collaborative 3D human actions. To address H2IAD, we then propose Interaction Anomaly Detection Network (IADNet), which is formalized with a Temporal Attention Sharing Module (TASM). Specifically, in designing TASM, we share the encoded motion embeddings across both people such that collaborative motion correlations can be effectively synchronized. Moreover, we notice that in addition to temporal dynamics, human interactions are also characterized by spatial configurations between two people. We thus introduce a Distance-Based Relational Encoding Module (DREM) to better reflect social cues in H2IAD. The normalizing flow is eventually employed for anomaly scoring. Extensive experiments on human-human motion benchmarks demonstrate that IADNet outperforms existing Human-centric AD baselines in H2IAD.
Abstract (translated)
人类中心的异常检测（AD）主要研究单个人的异常行为。然而，由于人类本质上倾向于协作行动，因此人的行为异常也可能源于人与人之间的互动。使用现有的单人AD模型来检测此类异常可能会导致准确性较低，因为这些方法通常不设计用来捕捉复杂和不对称的人际动态。 在本文中，我们引入了一个新的任务——人-人人交互异常检测（H2IAD），旨在识别协作3D人体动作中的异常交互行为。为解决H2IAD问题，我们提出了交互异常检测网络（IADNet），该网络采用了时间注意力共享模块（TASM）进行形式化设计。具体来说，在设计TASM时，我们将编码后的运动嵌入信息在两个人之间共享，以便有效地同步协作的运动相关性。 此外，我们注意到除了时间动态特性之外，人的互动还由两人之间的空间配置所定义。因此，我们引入了基于距离的关系编码模块（DREM），以更好地反映H2IAD中的社会线索。最后，使用归一化流技术进行异常评分。 在人类-人类动作基准测试的广泛实验中，结果显示IADNet优于现有的以人为中心的AD基线模型，在H2IAD任务上表现出色。
URL
https://arxiv.org/abs/2512.13560
