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CVPR2025Top-tier venue

Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection

Wenqiao Li, Yao Gu, Xintao Chen, Xiaohao Xu, Ming Hu, Xiaonan Huang, Yingna Wu

2025Year

Abstract

Band (b) Interaction (a) Object (c) Video with Physical Dynamics Figure 1. Towards visual discrimination of physical dynamics in real-world industrial object anomaly detection. We illustrate objects, interactions, and time-sequenced videos from the Physics-Grounded Anomaly Detection dataset: (a) Object; (b) Interaction: Applied actions shown with directional arrows; (c) Video with Physical Dynamics: Temporal sequences showing normal and abnormal states, highlighting anomalies like leaks, misalignments, and cracks. By focusing on the dynamic behaviors of complex objects, we enhance understanding of interactions and failure modes in real-world settings, where both structure and motion contribute to anomaly detection.

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