Papers › Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Alessandro Flaborea, Luca Collorone, Guido D'Amely, Stefano D'arrigo, Bardh Prenkaj, Fabio Galasso
Anomalies are rare and anomaly detection is often therefore framed as One-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset'ness of anomalies. But normalcy shares the same openset'ness property since humans can perform the same action in several ways, which the leading techniques neglect. We propose a novel generative model for video anomaly detection (VAD), which assumes that both normality and abnormality are multimodal. We consider skeletal representations and leverage state-of-the-art diffusion probabilistic models to generate multimodal future human poses. We contribute a novel conditioning on the past motion of people and exploit the improved mode coverage capabilities of diffusion processes to generate different-but-plausible future motions. Upon the statistical aggregation of future modes, an anomaly is detected when the generated set of motions is not pertinent to the actual future. We validate our model on 4 established benchmarks: UBnormal, HR-UBnormal, HR-STC, and HR-Avenue, with extensive experiments surpassing state-of-the-art results.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | UBnormal | MoCoDAD | AUC | 68.3% | #6 of 14 | Archive leaderboard | report |
| Video Anomaly Detection | HR-Avenue | MoCoDAD | AUC | 89.0 | #2 of 11 | Archive leaderboard | report |
| Video Anomaly Detection | HR-ShanghaiTech | MoCoDAD | AUC | 77.6 | #5 of 14 | Archive leaderboard | report |
| Video Anomaly Detection | HR-UBnormal | MoCoDAD | AUC | 68.4 | #1 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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