  Skeleton-based video anomaly detection (SVAD) is a crucial task in computer
vision. Accurately identifying abnormal patterns or events enables operators to
promptly detect suspicious activities, thereby enhancing safety. Achieving this
demands a comprehensive understanding of human motions, both at body and region
levels, while also accounting for the wide variations of performing a single
action. However, existing studies fail to simultaneously address these crucial
properties. This paper introduces a novel, practical and lightweight framework,
namely Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video
Anomaly Detection (GiCiSAD) to overcome the challenges associated with SVAD.
GiCiSAD consists of three novel modules: the Graph Attention-based Forecasting
module to capture the spatio-temporal dependencies inherent in the data, the
Graph-level Jigsaw Puzzle Maker module to distinguish subtle region-level
discrepancies between normal and abnormal motions, and the Graph-based
Conditional Diffusion model to generate a wide spectrum of human motions.
Extensive experiments on four widely used skeleton-based video datasets show
that GiCiSAD outperforms existing methods with significantly fewer training
parameters, establishing it as the new state-of-the-art.
