RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring
RealVDeblur is an efficient generative framework that addresses real-world video deblurring by synthesizing realistic training data via a physically grounded blur pipeline using 3D Gaussian Splatting, and leveraging a video diffusion prior with one-step distillation for robust inference on long videos.
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[Submitted on 22 Jul 2026]
Title:RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring
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Abstract: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 scene-level 3D Gaussian Splatting (3DGS) assets and high-frame-rate videos, providing realistic training data covering both camera-induced and object-motion blur. Second, a video diffusion prior is leveraged for restoration; to better accommodate frame-dependent blur variations, temporal compression in the VAE is disabled and a frame-wise encoding scheme is adopted. For practical deployment on long videos, multi-step diffusion sampling is distilled into an efficient one-step generator, and a training-free Temporal Window Mask stabilizes inference beyond the training horizon with constant memory usage. Extensive experiments on diverse real-world benchmarks demonstrate strong perceptual quality, semantic fidelity, and temporal consistency on unseen videos, as well as improved robustness in downstream 3D reconstruction under severe motion blur. Project page: this https URL
Comments: Project page with code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.20628 [cs.CV]
(or arXiv:2607.20628v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.20628
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Renbiao Jin [view email] [v1] Wed, 22 Jul 2026 18:01:03 UTC (5,510 KB)
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