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

arXiv:2609.18836 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-16

Terminology

• Verifier — 21× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Diffusion Model — 11× in this paper · explained in 8 episodes
• Grounding — 9× in this paper · explained in 3 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Fidelity — 6× in this paper · explained in 2 episodes
• Anomaly-aware Video Restoration — 4× in this paper
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Optical Flow — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Training-Free Framework — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Agentic Reasoning — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 15 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Coherent Video Inpainting Using Optical Flow-Guided Efficient Diffusion
• SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere
• MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios
• DiffuEraser: A Diffusion Model for Video Inpainting
• Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
• No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detection
• Efficient Video Diffusion Models: Advancements and Challenges
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection
• Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

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