mlsanityData to 7 Oct 2026

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

arXiv 2403.12172 ↗First posted 18 Mar 2024cs.CV cs.AIcited by 9

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 46 of its 75 references that are in the map. First posted on arXiv 18 Mar 2024.

Builds on

46 of 75 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection14 Jul

Other references · 45

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33 more
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  2. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  3. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
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  12. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  13. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 103
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  21. 2019Multi-timescale Trajectory Prediction for Abnormal Human Activity Detectioncited by 59
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 2.

9 papers.

  1. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  2. 2024DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detectioncited by 7
  3. 2024Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detectioncited by 6
  4. 2024Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detectioncited by 3
  5. 2026Bounding-Box Trajectories Matter for Video Anomaly Detectioncited by 3
  6. 2025Emotion Recognition from Skeleton Data: A Comprehensive Surveycited by 2
  7. 2025Zero-Shot Anomaly Detection with Dual-Branch Prompt Selectioncited by 1
  8. 2026Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Modelscited by 1
  9. 20253D Human-Human Interaction Anomaly Detectioncited by 0

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  3. 2023Exploring Diffusion Models for Unsupervised Video Anomaly Detection
  4. 2024Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
  5. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach
  6. 2024MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation
  7. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
  8. 2024DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector