# Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection Source: [Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection]() Collection index: [llms.txt]() Data updated: 2026-05-27T05:00:13.467Z Authors: Jiaxuan Liu, Yunkang Cao, Yufeng Chen, Chunyang Li, Yuhuan Du, Hui Zhang Published: 2026-05-25T04:05:25.000Z ## Abstract The deployment of zero-shot anomaly detection (AD) in embodied industrial inspection is severely bottlenecked by its reliance on passive, fixed-viewpoint 2D imagery. Such formulations inherently fail to accommodate the active, dynamic observations required in real-world environments. To break this limitation, we introduce Real-to-Twin Anomaly Detection, a novel task that evaluates physical observations directly against geometrically matched CAD Digital Twins. To tackle this new task, we propose AVATAR, a framework designed to learn robust semantic alignment between Real and Digital Twins. By bridging benign Sim2Real domain gaps using only defect-free pairs, AVATAR effectively transforms CAD priors into dynamic, anomaly-free references. This elegant formulation enables the model to localize diverse anomalies in a zero-shot manner as unalignable deviations, eliminating the need for defect annotations. Extensive experiments demonstrate that AVATAR substantially outperforms adapted state-of-the-art baselines, exhibiting exceptional robustness to severe viewpoint variations. The code and dataset will be made publicly available. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image-level Anomaly Detection on R2T (test)](): Image-level Anomaly Detection; R2T (test); I-AUROC: 71.15. - [Pixel-level Anomaly Localization on R2T (test)](): Pixel-level Anomaly Localization; R2T (test); P-AUROC: 90.55. ## Original sources and implementation [Original paper]() [PDF]()