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arxiv: 2604.10551 · v1 · submitted 2026-04-12 · 💻 cs.CV

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NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results

Bingchen Li, Boqi Zhang, Chao Zhou, Chengxi Zeng, Chen Lu, David Bull, Dehao Feng, Fan Zhang, Fengkai Zhang, Guoyi Xu, Hang Song, Hao Chen, Hao Kang, Haoran Bai, Huadong Ma, Huiyuan Fu, Jiachao Gong, Jiachen Tu, Jiahui Liu, Jiajia Liu, Jiangxin Dong, Jiawei Shi, Jing Yang, Jingyi Xu, Jingyu Ma, Jinshan Pan, Jiyuan Zhang, Junyang Chen, Kun Liu, Kun Yuan, Lei Lei, Linfeng Li, Long Sun, Mai Xu, Meisong Zheng, Qi Xu, Radu Timofte, Shang-Quan Sun, Shengxi Li, Shibo Yin, Shinan Chen, Shiyao Xiong, Shuai Liu, Sibin Deng, Suhang Yao, Tianhao Peng, Wei Zhou, Wenqi Ren, Xiaotao Wang, Xiaoxu Chen, Xijun Wang, Xilei Zhu, Xindong Zhang, Xin Li, Xinzhe Zhu, Xiuhao Qiu, Xu Cheng, Yahui Wang, Yanan Xing, Yaokun Shi, Yaoxin Jiang, Yeying Jin, Yibin Huang, Yilian Zhong, Ying Chen, Yixing Yang, Yixin Yang, Yizhen Shao, Yulin Ren, Yushun Fang, Yuxiang Chen, Yuxuan Jiang, Zhaokun Hu, Zhibo Chen, Zhijie Ma, Zhirui Liu, Zhuoya Zou, Zihong Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords short-form video restorationuser-generated contentgenerative modelsNTIRE challengeKwaiVIR benchmarkvideo enhancementsubjective evaluation
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The pith

The NTIRE 2026 Challenge shows that generative methods restore short-form UGC videos effectively on the new KwaiVIR benchmark.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper overviews the NTIRE 2026 Challenge on restoring short-form user-generated content videos in the wild with generative models. It introduces the KwaiVIR benchmark containing 200 synthetic training videos, 48 wild training videos, plus validation and test sets drawn from real-world sources. The challenge runs two tracks: a primary subjective track scored by user studies and a secondary objective track using standard metrics. Twelve teams submitted final solutions after 95 registrations, and those methods delivered strong results across the benchmark.

Core claim

The submitted methods achieved strong performance on the KwaiVIR benchmark, demonstrating encouraging progress in short-form UGC video restoration in the wild through generative-model approaches evaluated on both synthetic and real degradations.

What carries the argument

The KwaiVIR benchmark dataset paired with a dual-track evaluation that combines user-study subjective scoring and objective metrics to rank generative restoration outputs.

Load-bearing premise

The KwaiVIR mix of synthetic and real-world short videos plus the user-study ranking accurately reflects the complex degradations found in actual short-form UGC content.

What would settle it

New tests on a fresh collection of unseen wild short-form videos where top challenge entries fail to match or exceed prior methods in user preference scores.

Figures

Figures reproduced from arXiv: 2604.10551 by Bingchen Li, Boqi Zhang, Chao Zhou, Chengxi Zeng, Chen Lu, David Bull, Dehao Feng, Fan Zhang, Fengkai Zhang, Guoyi Xu, Hang Song, Hao Chen, Hao Kang, Haoran Bai, Huadong Ma, Huiyuan Fu, Jiachao Gong, Jiachen Tu, Jiahui Liu, Jiajia Liu, Jiangxin Dong, Jiawei Shi, Jing Yang, Jingyi Xu, Jingyu Ma, Jinshan Pan, Jiyuan Zhang, Junyang Chen, Kun Liu, Kun Yuan, Lei Lei, Linfeng Li, Long Sun, Mai Xu, Meisong Zheng, Qi Xu, Radu Timofte, Shang-Quan Sun, Shengxi Li, Shibo Yin, Shinan Chen, Shiyao Xiong, Shuai Liu, Sibin Deng, Suhang Yao, Tianhao Peng, Wei Zhou, Wenqi Ren, Xiaotao Wang, Xiaoxu Chen, Xijun Wang, Xilei Zhu, Xindong Zhang, Xin Li, Xinzhe Zhu, Xiuhao Qiu, Xu Cheng, Yahui Wang, Yanan Xing, Yaokun Shi, Yaoxin Jiang, Yeying Jin, Yibin Huang, Yilian Zhong, Ying Chen, Yixing Yang, Yixin Yang, Yizhen Shao, Yulin Ren, Yushun Fang, Yuxiang Chen, Yuxuan Jiang, Zhaokun Hu, Zhibo Chen, Zhijie Ma, Zhirui Liu, Zhuoya Zou, Zihong Chen.

Figure 1
Figure 1. Figure 1: The overall framework of Team RedMediaTech. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed video restoration framework built upon the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The DiT design of the proposed video restoration frame [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The overall framework of Team BVI. combines the high pixel-level fidelity of SeedVR2 with the perceptual quality and temporal consistency offered by FlashVSR. 4.8. IMAG@NJUST This team proposes a diffusion-based video restoration framework centered on CoDiVSR, titled “Rethinking What to Condition and What to Disentangle in Diffusion-based Video Super-Resolution”. For the objective track, they adopt a singl… view at source ↗
read the original abstract

This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-UGC) video restoration benchmark, termed KwaiVIR, which is contributed by USTC and Kuaishou Technology. It contains both synthetically distorted videos and real-world short-form UGC videos in the wild. For this edition, the released data include 200 synthetic training videos, 48 wild training videos, 11 validation videos, and 20 testing videos. The primary goal of this challenge is to establish a strong and practical benchmark for restoring short-form UGC videos under complex real-world degradations, especially in the emerging paradigm of generative-model-based S-UGC video restoration. This challenge has two tracks: (i) the primary track is a subjective track, where the evaluation is based on a user study; (ii) the second track is an objective track. These two tracks enable a comprehensive assessment of restoration quality. In total, 95 teams have registered for this competition. And 12 teams submitted valid final solutions and fact sheets for the testing phase. The submitted methods achieved strong performance on the KwaiVIR benchmark, demonstrating encouraging progress in short-form UGC video restoration in the wild.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. It introduces the KwaiVIR benchmark contributed by USTC and Kuaishou, containing 200 synthetic training videos, 48 wild training videos, 11 validation videos, and 20 test videos. The challenge features two tracks: a primary subjective track evaluated via user study and a secondary objective track. 95 teams registered, with 12 submitting valid final solutions and fact sheets; the paper states that these methods achieved strong performance on the KwaiVIR benchmark, demonstrating encouraging progress in generative-model-based restoration of short-form UGC videos under complex real-world degradations.

Significance. If the results hold, this work is significant for establishing a dedicated benchmark for short-form user-generated content video restoration, an area of growing practical importance for social media and mobile platforms. The inclusion of both synthetic and real-world wild videos, combined with dual subjective-objective evaluation tracks, provides a more comprehensive assessment framework than purely objective metrics alone. The high registration rate and 12 submissions indicate community engagement and can help drive advancements in generative approaches to handling authentic degradations.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'the submitted methods achieved strong performance' and demonstrate 'encouraging progress' is not supported by any quantitative metrics (e.g., user-study mean opinion scores, PSNR/SSIM values, or comparisons against baselines or prior methods). Without these numbers, error bars, or ranking tables, the strength of the performance claim cannot be independently assessed.
  2. [Dataset description / Evaluation] Dataset and evaluation sections: The benchmark relies on only 48 wild training videos and 20 test videos for the in-the-wild track. This limited scale raises questions about whether the reported progress generalizes to the diversity of real-world short-form UGC degradations, directly affecting the reliability of the 'strong performance' conclusion.
minor comments (3)
  1. [Evaluation] The paper should provide explicit details on the user-study protocol (number of participants, rating scale, statistical significance testing) to allow readers to interpret the subjective track results.
  2. [Results] A summary table listing the top teams, their methods, and key scores (both subjective and objective) would improve clarity and allow direct comparison of contributions.
  3. [Throughout] Minor typographical inconsistencies in video counts or track descriptions between the abstract and main text should be reconciled for precision.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our NTIRE 2026 challenge overview paper. We address each major comment below and propose targeted revisions where appropriate to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'the submitted methods achieved strong performance' and demonstrate 'encouraging progress' is not supported by any quantitative metrics (e.g., user-study mean opinion scores, PSNR/SSIM values, or comparisons against baselines or prior methods). Without these numbers, error bars, or ranking tables, the strength of the performance claim cannot be independently assessed.

    Authors: We agree that the abstract, as a high-level summary, presents the performance claim qualitatively without specific numbers. The full manuscript contains dedicated results sections that report quantitative outcomes from both tracks, including mean opinion scores from the user study for the primary subjective track, PSNR/SSIM and other objective metrics for the secondary track, ranking tables of the 12 submitted solutions, and comparisons against standard baselines and prior generative restoration methods. These details support the 'strong performance' description relative to the challenge benchmark. To improve clarity and allow independent assessment from the abstract alone, we will revise it to include key quantitative highlights (e.g., top MOS values and relative improvements) while keeping it concise. revision: yes

  2. Referee: [Dataset description / Evaluation] Dataset and evaluation sections: The benchmark relies on only 48 wild training videos and 20 test videos for the in-the-wild track. This limited scale raises questions about whether the reported progress generalizes to the diversity of real-world short-form UGC degradations, directly affecting the reliability of the 'strong performance' conclusion.

    Authors: We acknowledge the modest scale of the wild video portion (48 training + 20 test videos), which stems from the inherent challenges of sourcing and curating authentic short-form UGC content with diverse, complex real-world degradations while maintaining evaluation feasibility. The videos were deliberately selected by the benchmark contributors (USTC and Kuaishou) to span representative degradation types encountered in social media platforms. The challenge design mitigates scale limitations through its dual-track evaluation: the primary subjective user study provides perceptual assessment beyond objective metrics, and the synthetic track (200 videos) offers complementary controlled data. We will expand the manuscript with an explicit limitations paragraph discussing dataset scale, potential generalization caveats, and plans for future expansions in subsequent NTIRE editions. This does not alter the core benchmark but strengthens transparency around the 'strong performance' interpretation. revision: partial

Circularity Check

0 steps flagged

No significant circularity: descriptive competition overview

full rationale

The paper is a standard NTIRE challenge report that describes the KwaiVIR dataset construction, two evaluation tracks, participation numbers (95 registered, 12 valid submissions), and empirical outcomes of submitted methods. It contains no equations, derivations, fitted parameters presented as predictions, or load-bearing self-citations that reduce any claim to the authors' own inputs by construction. The statement that methods 'achieved strong performance' is a factual summary of competition results rather than a derived quantity. This matches the default expectation for non-derivational papers and warrants score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical challenge overview with no mathematical modeling, so the ledger is empty.

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discussion (0)

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Reference graph

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