DecoGen: Zero-Shot Cross-Style Industrial Anomaly Generation via Explicit Physical Semantic Constraint

In recent years, industrial anomaly image generation has shown immense potential in alleviating the scarcity of real defect data. However, existing few-shot generation methods typically rely on implicit and coarse-grained visual guidance lacking real-world physical grounding, which inherently entangles anomalous features with domain-specific background textures. This representation entanglement leads to severe texture contamination and semantic misalignment, making it exceedingly difficult to transfer known defects to novel product styles that lack real anomaly images. To address these issues, we propose DecoGen, a novel zero-shot cross-style anomaly transfer framework driven by explicit physical semantic constraints. Specifically, we construct a structured anomaly prompt bank to explicitly decompose defects into multi-dimensional, domain-invariant physical properties for flexible semantic combinations. Furthermore, a physical attribute aligner is designed to enforce rigorous disentangled alignment in the feature space, thoroughly decoupling the pure physical anomaly appearance from both the source background texture and its mask-derived spatial support. Experiments show that DecoGen yields high-fidelity and diverse cross-style anomalies. Crucially, our synthesized data significantly improves the performance of downstream anomaly detectors on unseen product styles, increasing the average image-level and pixel-level AUROC by 3.99% and 6.32%, respectively.