DeCo: Zero-Shot Anomaly Generation through Decoupling and Recoupling

Industrial anomaly inspection is severely hindered by thescarcity of real anomalous data. Zero-shot industrial anomaly genera-tion addresses this by generating anomalies on specific products withoutrequiring any of their real anomalous images. However, existing meth-ods suffer from two critical limitations, i.e., inaccurate anomaly informa-tion acquisition and uncontrolled anomaly-product fusion. To overcomethese challenges, we propose DeCo, which decouples the anomaly struc-ture from its source product, and explicitly recouples it with the nor-mal textures of the target product. During anomaly information acquisi-tion, Dual-Routing Flow (DR-Flow) binds the texture-invariant anomalystructure to an abnormal token, while a parallel constraint, Product-Invariant Flow (PI-Flow), prevents the abnormal token from binding thesource product. During anomaly-product fusion, we propose a hybrid in-jection to recouple the acquired anomaly structure with the target prod-uct, and Product Compatibility Correction (PCC) to compensate for theincompatibility between the acquired anomaly structure and the product.Extensive experiments demonstrate that DeCo establishes a new state-of-the-art. Training downstream detection models on our generated datayields massive pixel AP improvements of 5.1% on MVTec AD and 8.2%on VisA. Code is available at https://github.com/HUST-SLOW/DeCo.