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This paper introduces DPA, a diffusion-based framework that facilitates zero-shot anomaly generation by decoupling product-agnostic anomaly representations, allowing for the transfer of real anomalies from existing products to new, unseen target products. By implementing an anomaly type filtering mechanism and an adaptive mask-guided pipeline, the method ensures that generated anomalies maintain both positional and geometric plausibility. Experimental results on multiple datasets, including MVTec-AD and VisA, show that DPA significantly enhances the realism of generated anomalies and improves downstream anomaly detection performance in both zero-shot and few-shot contexts.
Realistic anomalies can be generated for unseen products without needing any target-product samples, revolutionizing how we approach anomaly detection in industrial settings.
Industrial anomaly detection benefits from anomaly samples, yet newly deployed products typically provide only normal images, making anomaly samples difficult to collect. Zero-shot anomaly generation offers a promising solution which avoids collection of target-product anomalies. However, existing methods mainly rely on texture images or text descriptions as anomaly sources, which often produce unrealistic anomalies. Observing that similar anomalies can recur across different products, we propose anomaly transfer-based zero-shot generation, which reuses real anomalies from existing source products, making target-product anomalies no longer necessary to generate realistic anomalious samples for unseen target products. Since not every anomaly type suits the target product, an anomaly type filtering mechanism first selects plausible source types. To transfer selected anomaly, we propose DPA, a diffusion-based framework that decouples product-agnostic anomaly representations. Instead of directly extracting anomaly representations, DPA learns product-irrelevant anomaly embeddings through training with the mismatched data pair, enabling transferable anomaly concept learning across products. Furthermore, we design an adaptive mask-guided pipeline that leverages adaptive masks to control the positional and geometric plausibility of generated anomalies during generation. A training-free anomaly labeling module is further introduced to produce pixel-level annotations aligned with generated anomalies. Extensive experiments on MVTec-AD, VisA, and a dedicated anomaly-transfer benchmark demonstrate that the proposed setting and DPA generate more realistic anomalies and significantly improve downstream anomaly detection performance under both zero-shot and few-shot settings. Source code and models will be released.
