# Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection

arXiv: https://arxiv.org/abs/2412.17210 · HTML: https://mlsanity.com/paper/2412.17210/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm](https://mlsanity.com/paper/2004.07657/) (2020)
- [Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos](https://mlsanity.com/paper/1903.03295/) (2019)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [Space-Time-Separable Graph Convolutional Network for Pose Forecasting](https://mlsanity.com/paper/2110.04573/) (2021)
- [Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate](https://mlsanity.com/paper/2010.07110/) (2020)
- [STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection](https://mlsanity.com/paper/1804.08381/) (2018)
- [USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation](https://mlsanity.com/paper/2412.09220/) (2024)
- [Holistic Representation Learning for Multitask Trajectory Anomaly Detection](https://mlsanity.com/paper/2311.01851/) (2023)
- [Region-aware Image-based Human Action Retrieval with Transformers](https://mlsanity.com/paper/2407.09924/) (2024)

## Built on by (most cited first, 1 of 1)

- [DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detection](https://mlsanity.com/paper/2507.20629/) (2025)

## Abstract (arXiv)

Video Anomaly Detection (VAD) is essential for computer vision research. Existing VAD methods utilize either reconstruction-based or prediction-based frameworks. The former excels at detecting irregular patterns or structures, whereas the latter is capable of spotting abnormal deviations or trends. We address pose-based video anomaly detection and introduce a novel framework called Dual Conditioned Motion Diffusion (DCMD), which enjoys the advantages of both approaches. The DCMD integrates conditioned motion and conditioned embedding to comprehensively utilize the pose characteristics and latent semantics of observed movements, respectively. In the reverse diffusion process, a motion transformer is proposed to capture potential correlations from multi-layered characteristics within the spectrum space of human motion. To enhance the discriminability between normal and abnormal instances, we design a novel United Association Discrepancy (UAD) regularization that primarily relies on a Gaussian kernel-based time association and a self-attention-based global association. Finally, a mask completion strategy is introduced during the inference stage of the reverse diffusion process to enhance the utilization of conditioned motion for the prediction branch of anomaly detection. Extensive experiments on four datasets demonstrate that our method dramatically outperforms state-of-the-art methods and exhibits superior generalization performance.