Hierarchical Prototype-Conditioned Masked Diffusion for Multi-View Anomaly Detection

Multi-view anomaly detection leverages complementary observations to identify anomalies that may be occluded or weakly visible from a single viewpoint. However, existing methods often rely on dense patch-level cross-view interactions, which may introduce redundant and spatially inconsistent information. To address this limitation, we propose HPMD-AD, a Hierarchical Prototype-Conditioned Masked Diffusion model for multi-view Anomaly Detection, which models complementary cross-view normal patterns through compact hierarchical prototypes to guide normal feature reconstruction. Specifically, we progressively abstract features from each view into input-dependent view-level prototypes and aggregate them into sample-level prototypes, capturing view-specific normal patterns and cross-view shared structures without explicit spatial alignment. These hierarchical prototypes jointly guide diffusion-based feature reconstruction, while prototype masking and feature masking encourage complementary cross-view reasoning and mitigate identity shortcuts during reconstruction. Furthermore, we introduce a prototype-prior adaptive fusion strategy that jointly considers inter-view score disagreement and prototype reliability for robust multi-view score aggregation. Extensive experiments on Real-IAD and the substantially larger Real-IAD Variety benchmark demonstrate that HPMD-AD achieves state-of-the-art performance, attaining sample-level AUROC scores of 94.6% and 96.5%, respectively.