3 citations · 4 across the 16 of their papers we have counts for
15 papers · 1 filter
LPM: Industrial-Scale Generative Video Restoration
Bichuan Zhu, Fulin Li, Jiachao Gong +14
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,…
Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution
Hongbo Wang, Huaibo Huang, Pin Wang +3
Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limitation to a fundamental spectral misalignment between isotropic object…
Pioneering Perceptual Video Fluency Assessment: A Novel Task with Benchmark Dataset and Baseline
Qizhi Xie, Kun Yuan, Yunpeng Qu +3
Accurately estimating humans' subjective feedback on video fluency, e.g., motion consistency and frame continuity, is crucial for various applications like streaming and gaming. Ye…
Tuning Real-World Image Restoration at Inference: A Test-Time Scaling Paradigm for Flow Matching Models
Purui Bai, Junxian Duan, Pin Wang +4
Although diffusion-based real-world image restoration (Real-IR) has achieved remarkable progress, efficiently leveraging ultra-large-scale pre-trained text-to-image (T2I) models an…
Versatile Recompression-Aware Perceptual Image Super-Resolution
Mingwei He, Tongda Xu, Xingtong Ge +3
Perceptual image super-resolution (SR) methods restore degraded images and produce sharp outputs. In practice, those outputs are usually recompressed for storage and transmission.…
Score2Instruct: Scaling Up Video Quality-Centric Instructions via Automated Dimension Scoring
Qizhi Xie, Kun Yuan, Yunpeng Qu +5
Classical video quality assessment methods generate a numerical score to judge a video's perceived visual fidelity and clarity. Yet, a score fails to describe the video's complex q…