Predictive Transitions for Video Anomaly Detection

We derive a direct anomaly signal from a representation learned to condition future-frame generation, using a state–transition framework for normal-only video anomaly detection. Current appearance and ordered frame and feature changes jointly condition a diffusion Transformer. After generative training, we freeze the encoders and fit a small head to predict the next local frame change from transition features. Matched representation probes on Ped2, Avenue, and ShanghaiTech show that learned transition features outperform raw frame-change history for anomaly detection. Interventions reveal dependence on transition content and temporal order, with clearer temporal-order effects on Ped2 and Avenue. Together, the probes and conditioning ablations show that transition features can support anomaly detection even when hybrid conditioning does not improve frame-error AUC. The change score can be computed from the learned encoders and prediction head without iterative frame generation.