arXiv AI
PickMoment: Continuous-Time Single-Image-to-Video via Learning Deblurring and Blur-to-Video
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arXiv AI
Jul 1
FMA-Net++: Motion- and Exposure-Aware Joint Video Super-Resolution and Deblurring
arXiv:2512. 04390v2 Announce Type: replace-cross Abstract: Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure-duration variation, which changes the extent of motion blur across video frames.
arXiv AI
Jul 24
RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring
arXiv:2607. 20628v1 Announce Type: cross 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.
arXiv Computer Vision
Sep 10
SloMoDeblur: A Large-Scale Smartphone Image Deblurring Dataset
arXiv:2506.19445v5 Announce Type: replace Abstract: Motion blur remains one of the most common and visually disruptive degradations in real-world smartphone imaging, yet existing deblurring benchmark...
arXiv AI
Jul 24
Deblurring in the Wild: A Real-World Image Deblurring Dataset from Smartphone High-Speed Videos
arXiv:2506. 19445v4 Announce Type: cross Abstract: We introduce the largest real-world image deblurring dataset constructed from smartphone slow-motion videos.
arXiv AI
5d ago
GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow
The paper introduces GVCC, a zero‑shot video compression framework that uses a pretrained generative video model as the decoder. GVCC transforms deterministic rectified‑flow samplers into stochastic processes, enabling the transmission of compressed information through per‑step stochastic innovations. The authors evaluate three GVCC variants—Text‑to‑Video, Image‑to‑Video, and First‑Last‑Frame‑to‑Video—on the UVG dataset, reporting perceptual, fidelity, and temporal metrics without claiming global rate‑distortion gains.
arXiv Computer Vision
Sep 15
UniE2F: A Unified Diffusion Framework for Event-to-Frame Reconstruction with Video Foundation Models
arXiv:2602.19202v3 Announce Type: replace Abstract: Event cameras excel at high-speed, low-power, and high-dynamic-range scene perception. However, as they fundamentally record only relative intensit...