In 2026, video deblurring remains a critical challenge in computer vision, particularly for real-world applications such as mobile imaging and 3D reconstruction. The paper introduces RealVDeblur, an efficient generative framework designed to enhance robustness in diverse and unpredictable capture conditions. This work addresses three key hindrances to practical deployment: the scarcity of realistic training data, the computational expense of existing diffusion models, and the difficulty of maintaining temporal consistency across long video sequences.
Methodology
First, RealVDeblur constructs a large-scale, physically grounded blur synthesis pipeline using scene-level 3D Gaussian Splatting (3DGS) assets and high-frame-rate video sequences. This pipeline generates realistic training data that covers both camera-induced and object-motion blur, effectively bridging the gap between synthetic and real-world degradations.
Second, the framework leverages a video diffusion prior for restoration. To better accommodate frame-dependent blur variations, RealVDeblur disables temporal compression in the Variational Autoencoder (VAE) and adopts a frame-wise encoding scheme, preserving fine-grained temporal information.
For practical deployment on long videos, the authors distill the multi-step diffusion sampling process into an efficient one-step generator, drastically reducing inference time while maintaining high perceptual quality. Additionally, a training-free Temporal Window Mask is introduced to stabilize inference beyond the training horizon, using constant memory regardless of video length.
Results and Impact
Extensive experiments on diverse real-world benchmarks demonstrate that RealVDeblur achieves superior perceptual quality, semantic fidelity, and temporal consistency on unseen videos. The model also shows improved robustness in downstream tasks, such as 3D reconstruction under severe motion blur. The project page and code are publicly available at: https://rbjin.github.io/RealVDeblur.
Subjects
- Computer Vision and Pattern Recognition (cs.CV)
- Artificial Intelligence (cs.AI)
This work not only sets a new standard for real-world video deblurring but also paves the way for efficient, generalizable deployment in edge devices and augmented reality applications in 2026 and beyond.
via ArXiv CV