RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring
Quick Answer
RealVDeblur introduces an efficient generative framework for video deblurring, leveraging a large-scale blur synthesis pipeline and a one-step diffusion generator.
Quick Take
It achieves strong perceptual quality and temporal consistency in unseen videos, enhancing robustness for applications like mobile imaging and 3D reconstruction under severe motion blur.
Key Points
- Constructs a large-scale blur synthesis pipeline using 3D Gaussian Splatting assets.
- Utilizes a video diffusion prior for effective restoration under diverse conditions.
- Employs a one-step generator for efficient long video processing.
- Demonstrates improved robustness in downstream 3D reconstruction tasks.
- Achieves strong perceptual quality and temporal consistency on real-world benchmarks.
Paper Resources
Source Excerpt
Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet robust restoration is critical for downstream pipelines such as mobile imaging and 3D reconstruction. This work presents \textbf{RealVDeblur}, an efficient generative framework designed to improve in-the-wild robustness under diverse real capture conditions. First, a large-scale, physically grounded blur synthesis pipeline is constructed from scen
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