  This paper introduces a method for zero-shot video restoration using
pre-trained image restoration diffusion models. Traditional video restoration
methods often need retraining for different settings and struggle with limited
generalization across various degradation types and datasets. Our approach uses
a hierarchical token merging strategy for keyframes and local frames, combined
with a hybrid correspondence mechanism that blends optical flow and
feature-based nearest neighbor matching (latent merging). We show that our
method not only achieves top performance in zero-shot video restoration but
also significantly surpasses trained models in generalization across diverse
datasets and extreme degradations (8$\times$ super-resolution and high-standard
deviation video denoising). We present evidence through quantitative metrics
and visual comparisons on various challenging datasets. Additionally, our
technique works with any 2D restoration diffusion model, offering a versatile
and powerful tool for video enhancement tasks without extensive retraining.
This research leads to more efficient and widely applicable video restoration
technologies, supporting advancements in fields that require high-quality video
output. See our project page for video results at
https://jimmycv07.github.io/DiffIR2VR_web/.
