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arXiv · 2605.25407

Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection

Jiaxuan Liu·Yunkang Cao·Yufeng Chen·Chunyang Li·Yuhuan Du·Hui Zhang
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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.

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Jiaxuan Liu, Yunkang Cao, Yufeng Chen, Chunyang Li, Yuhuan Du, Hui Zhang. 2026-05-25. Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection. https://arxiv.org/abs/2605.25407

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