Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restoration

2026-09-16

AI Summary

本文提出AVR，利用预训练模型修复视频中的异常区域，仅在无证据可复制时生成内容，通过实验验证其有效性。

Abstract

A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomaly detectors stop at a score or a label, while training-free video editing answers to a user prompt rather than to a detector. This paper proposes AVR (Anomaly-aware Video Restoration), which closes that gap with frozen pretrained models alone and generates content only where the clip offers no evidence to copy. Motion evidence first gates open-vocabulary proposals into spatio-temporal masks. A background prior computed from the clip then fills every pixel the anomaly ever uncovers, leaving diffusion to synthesize only what no frame showed, and a frozen verifier decides per clip whether to trust a classical, a prior-anchored, or a background-conditioned restorer. Extensive experiments on three surveillance datasets, under both full-reference anomaly injection and real anomalies, show that AVR leads full-frame fidelity under oracle masks, matches three trained video inpainters inside the edited region, and outperforms a detect-then-generate pipeline on the masks it produces itself, while suppressing both the residual anomaly and the flicker of free diffusion.

Problem

• anomaly detection
• video restoration
• training-free
• surveillance system

Innovation

• Anomaly-aware Video Restoration
• frozen pretrained models
• spatio-temporal masks
• background prior
• diffusion synthesis