arXiv Machine Learning July 3, 2026 By Miko{\l}aj Jastrz\k{e}bski, Dawid Glinkowski, Dawid Zieli\'nski, Daniel Borkowski, Wojciech Koz{\l}owski, Kamil Adamczewski

AbsoluteDegradation: A Physics-Inspired Synthetic Film-Degradation Pipeline and Archival Film Restoration Benchmark

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arXiv:2607. 02131v1 Announce Type: cross Abstract: Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation benchmarks.

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benchmarks

arXiv Machine Learning

Jul 24

DART: A Degradation-Aware Recurrent Transformer for Archival Film Restoration

arXiv:2607. 21219v1 Announce Type: cross Abstract: Archival film restoration is a challenging problem because historical footage contains compound degradations such as scratches, dust, blur, noise, flicker, and photometric aging, while clean reference videos are unavailable.

By Miko{\l}aj Jastrz\k{e}bski, Wojciech Koz{\l}owski, Kamil Adamczewski

llms benchmarks

arXiv Computer Vision

4d ago

ArtifactWorld: Scaling 3D Gaussian Splatting Artifact Restoration via Video Generation Models

arXiv:2604.12251v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) delivers high-fidelity real-time rendering but suffers from geometric and photometric degradations under sparse-view c...

By Xinliang Wang, Yifeng Shi, Zhenyu Wu

diffusion benchmarks safety

Hugging Face Trending Papers

Jul 15

LPM: Industrial-Scale Generative Video Restoration

We present the Large Processing Model (LPM), a diffusion-based generative framework for photorealistic video restoration under complex, in-the-wild degradations. To our knowledge, LPM is the first generative video restoration model deployed at industrial scale.

diffusion data-engineering

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.

By Renbiao Jin, Mingxin Yang, Yutian Chen, Junhao Zhuang, Xin Cai, Mulin Yu, Linning Xu, Wenxian Yu, Danping Zou, Shi Guo, Tianfan Xue

diffusion benchmarks

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...

By Gang Xu, Zhiyu Zhu, Junhui Hou

diffusion

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.

By Geunhyuk Youk, Jihyong Oh, Munchurl Kim

rag efficiency benchmarks