LVTINO: LAtent Video consisTency INverse sOlver for High Definition Video Restoration

2025-10-01

AI Summary

Existing high-resolution video restoration methods suffer from insufficient spatial detail recovery and weak temporal dependency modeling, leading to inter-frame inconsistency. Method: We propose the first zero-shot inverse solving framework based on Video Consistency Models (VCMs), explicitly encoding temporal causality—departing from frame-wise latent diffusion model (LDM) priors. Our approach employs a distilled video latent diffusion model as a structural prior, jointly enforcing measurement consistency and latent-space optimization without backpropagation; reconstruction is achieved via only a few forward passes. Contribution/Results: The method achieves state-of-the-art performance across diverse video inverse problems—including super-resolution, deblurring, and frame interpolation—significantly improving perceptual quality and temporal coherence while maintaining high computational efficiency. It establishes a new benchmark for zero-shot video restoration.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Computational imaging methods increasingly rely on powerful generative diffusion models to tackle challenging image restoration tasks. In particular, state-of-the-art zero-shot image inverse solvers leverage distilled text-to-image latent diffusion models (LDMs) to achieve unprecedented accuracy and perceptual quality with high computational efficiency. However, extending these advances to high-definition video restoration remains a significant challenge, due to the need to recover fine spatial detail while capturing subtle temporal dependencies. Consequently, methods that naively apply image-based LDM priors on a frame-by-frame basis often result in temporally inconsistent reconstructions. We address this challenge by leveraging recent advances in Video Consistency Models (VCMs), which distill video latent diffusion models into fast generators that explicitly capture temporal causality. Building on this foundation, we propose LVTINO, the first zero-shot or plug-and-play inverse solver for high definition video restoration with priors encoded by VCMs. Our conditioning mechanism bypasses the need for automatic differentiation and achieves state-of-the-art video reconstruction quality with only a few neural function evaluations, while ensuring strong measurement consistency and smooth temporal transitions across frames. Extensive experiments on a diverse set of video inverse problems show significant perceptual improvements over current state-of-the-art methods that apply image LDMs frame by frame, establishing a new benchmark in both reconstruction fidelity and computational efficiency.

Problem

• Extending diffusion models to high-definition video restoration
• Ensuring temporal consistency in video frame reconstruction
• Achieving efficient zero-shot video inverse solving

Innovation

• Leverages Video Consistency Models for temporal dependencies
• Bypasses automatic differentiation for computational efficiency
• Ensures measurement consistency and smooth frame transitions