  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.
