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REVIEW 4 major objections 4 minor 84 references

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The challenge claims the best face restoration splits the task into identity fidelity, texture realism, and naturalness, and ranks entries by a weighted perceptual-quality score.

desk verdict A transparent NTIRE challenge report useful as a recipe survey and benchmark record, but its ranking is a metric-optimization artifact, not a proven perceptual advance. read the letter →

arxiv 2504.14600 v1 pith:UELKCLNC submitted 2025-04-20 cs.CV

Zheng Chen , Jingkai Wang , Kai Liu , Jue Gong , Lei Sun , Zongwei Wu , Radu Timofte , Yulun Zhang
show 46 more authors
Jianxing Zhang Jinlong Wu Jun Wang Zheng Xie Hakjae Jeon Suejin Han Hyung-Ju Chun Hyunhee Park Zhicun Yin Junjie Chen Ming Liu Xiaoming Li Chao Zhou Wangmeng Zuo Weixia Zhang Dingquan Li Kede Ma Yun Zhang Zhuofan Zheng Yuyue Liu Shizhen Tang Zihao Zhang Yi Ning Hao Jiang Wenjie An Kangmeng Yu Chenyang Wang Kui Jiang Xianming Liu Junjun Jiang Yingfu Zhang Gang He Siqi Wang Kepeng Xu Zhenyang Liu Changxin Zhou Shanlan Shen Yubo Duan Yiang Chen Jin Guo Mengru Yang Jen-Wei Lee Chia-Ming Lee Chih-Chung Hsu Hu Peng Chunming He
This is my paper · ORCID
classification cs.CV
keywords real-worldfacerestorationblindperceptualimagequalityassessmentidentitypreservationdiffusion-basedTransformerpriorschallengeno-referenceIQA
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports the 2025 real-world face restoration challenge, in which 141 people registered, 13 teams submitted working models, and 10 teams passed the identity gate and received final scores. Its central claim is that the ranking produced by the organizers' weighted perceptual-quality score identifies the current best practice for restoring degraded faces, with identity preservation enforced as a pass/fail condition rather than part of the score. The report's main conclusions are that diffusion-based generative priors are the primary source of realistic texture, Transformer-based priors are the main safeguard of identity, and the winning approach combines several generative priors in a serial pipeline rather than relying on one model. If the ranking is valid, these three trends describe the field's current best practice.

What carries the argument

The load-bearing object is the challenge's evaluation formula, a single perceptual score built from six quality measures (CLIPIQA, MANIQA, MUSIQ, Q-Align, NIQE, and FID against the FFHQ distribution), gated by an identity check from a pre-trained face-recognition model (AdaFace) with per-dataset cosine thresholds: 0.3 for WIDER-Test and WebPhoto-Test, 0.6 for LFW-Test and CelebChild-Test, and 0.5 for CelebA. This score converts the subjective goal of natural, identity-preserving restoration into one number, and every conclusion in the report about which methods win and which trends matter is derived from it. The identity gate does not enter the score; it only decides whether a submission is ranked at all.

What would settle it

Recompute the Table 1 rankings without the CLIPIQA term: the second-place team's score becomes $4.3561 - 0.9337 = 3.4224$, which beats the winner's $4.3638 - 0.9517 = 3.4121$, so the reported state of the art changes with the removal of a single metric; a human side-by-side preference test on the 450 test images would show which ranking, if either, matches perception.

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Extended reading notes

Core claim

The central discovery, in the organizers' telling, is that the winning formula for real-world face restoration is not a single architecture but a staged division of labour: first enforce faithfulness to the input identity with a StyleGAN-based generative prior, then add realistic texture with a diffusion module adapted from DiffBIR (a diffusion-based blind image restoration prior), then re-cast the result as an image-reconstruction problem and steer a VAE latent space with vision foundation models to make the texture-rich output look like a natural photograph. The challenge's evaluation procedure supports this ranking by first requiring every submission to pass an identity-similarity check (AdaFace cosine similarity, with dataset-specific thresholds and at most 10 failures), and then ranking the survivors by the weighted score $$\mathrm{Score} = \mathrm{CLIPIQA} + \mathrm{MANIQA} + \frac{\mathrm{MUSIQ}}{100} + \frac{\mathrm{QALIGN}}{5} + \max\left(0, \frac{10-\mathrm{NIQE}}{10}\right) + \max\left(0, \frac{100-\mathrm{FID}}{100}\right).$$ The report argues that diffusion-based methods now dominate for realism, Transformer-based codebook priors dominate for identity, and multi-model progressive fusion is the emerging best practice.

Load-bearing premise

The whole ranking stands on the assumption that the hand-composed weighted score—CLIPIQA, MANIQA, MUSIQ, Q-Align, NIQE, and FID terms, with per-dataset AdaFace identity thresholds—faithfully captures what people mean by a natural, identity-preserving face restoration.

Editorial extensions

If this is right

  • The winning three-stage recipe—identity first via a GAN prior, texture second via a diffusion prior, naturalness third via a VAE guided by a vision foundation model—defines the current template for high-scoring face restoration.
  • Diffusion-based restoration modules, especially one-step variants distilled from models like SDXL-Turbo, are now fast enough (about 0.21 seconds per image on an A6000 in one entry) to serve as practical components rather than research curiosities.
  • Because no-reference IQA metrics such as CLIPIQA and MUSIQ are part of both the score and several teams' inference-time optimization, future challenge entries will likely treat these metrics as training and inference objectives directly.
  • Transformer-based codebook priors remain the standard way to keep identity stable when input faces are heavily degraded, even when the final texture is produced by a diffusion model.
  • If the claimed ranking is correct, the top ten entries collectively set a new benchmark for real-world face restoration on the five test datasets (CelebChild-Test, LFW-Test, WIDER-Test, CelebA, WebPhoto-Test).

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The fairness rules declare that applying no-reference IQA metrics and simulated degradation pipelines during training and testing is fair practice, so the contest is partly a competition in metric optimization; an independent human-preference study would be needed to know whether the top-ranked outputs are also the ones people prefer.
  • The top two teams in Table 1 are separated by only 0.0077 score points, and removing the CLIPIQA term alone would flip their order, so the reported 'leading trend' is sensitive to the hand-chosen weights of the formula.
  • The modular divide-and-conquer recipe of the winner—fidelity, then texture, then naturalness—could transfer to other object-class restoration tasks where faithful identity and photorealistic detail pull against each other, such as text, animals, or documents.
  • Because the identity gate allows up to 10 failures per submission, a system could deliberately spend that slack on hard images to chase perceptual score, a strategy the ranking would reward.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper reports the NTIRE 2025 challenge on real-world face restoration. It describes the challenge setup (450 test images from five face datasets), the evaluation protocol, the weighted image quality score used for ranking, and the submitted solutions, with detailed descriptions of the top five teams and brief notes on the remaining teams. The central claim is that the resulting ranking identifies the current state of the art in real-world face restoration and that the submitted techniques, particularly diffusion-based realism, transformer-based identity preservation, and multi-model fusion, advance the field.

Significance. If the ranking is valid, this is a useful archival record for the community: the paper ships evaluation scripts, source code links, pretrained models, and a structured comparison of thirteen methods on a common real-world benchmark. The qualitative trends reported, such as diffusion for realism and transformers for identity, are plausible and consistent with the wider literature. The main scientific limitation is that the headline conclusions rest entirely on a composite no-reference IQA score whose validity for face restoration is not established, and whose components several participants optimized either during training or at inference time.

major comments (4)
  1. [Section 2.2.2 / Section 3.3 / Sections 4.2-4.4] The ranking is computed from a weighted sum of CLIPIQA, MANIQA, MUSIQ, Q-Align, NIQE, and FID, yet Section 3.3 explicitly declares no-reference IQA during training and testing to be fair practice. Sections 4.2-4.4 show that three of the top five teams used these exact metrics: PISA-MAP performs MAP estimation in diffusion latents guided by CLIPIQA and MUSIQ, MiPortrait uses CLIPIQA/MUSIQ thresholds to activate GFPGAN, and AIIA trains with a CLIP-IQA loss. The reported ranking therefore partially measures optimization of the scoring metric itself, and the abstract's claim that the challenge 'advances the performance of real-world face restoration' is not substantiated without a human study or at least a cross-check on a held-out metric that no participant was allowed to optimize.
  2. [Section 2.2.2, Score formula] The Score formula is an ad-hoc weighted combination with no calibration or sensitivity analysis: Q-Align is divided by 5, NIQE and FID are truncated at zero after linear rescaling, while CLIPIQA and MANIQA enter directly. No justification is given for these coefficients or for the truncation points, and no analysis shows that the ranking is stable under plausible alternative weights. Since every conclusion about which method wins and which trends matter is derived from this formula, the paper should either calibrate the weights against human perceptual judgments or report a sensitivity analysis over the weights and thresholds.
  3. [Section 2.2.2, Step 1] The AdaFace identity gate uses per-dataset cosine thresholds of 0.3, 0.5, and 0.6, plus an allowance of up to 10 low-similarity failures, but the manuscript gives no rationale or calibration for these values. Because three teams were declared invalid and excluded from the ranking, the thresholds directly determine the final leaderboard. Without validation of these thresholds against human identity judgments, the paper's claim that the submissions 'maintain identity consistency' is not supported.
  4. [Table 1 and Section 3.4] The top-two margin is 0.0077 Score points (AllForFace 4.3638 vs. IIL 4.3561), but the paper reports no variance, no confidence intervals, and no human baseline for either the restored images or the original low-quality inputs. FID in particular is computed from only 450 test images against the FFHQ distribution and is known to be high-variance at this sample size. In the absence of repeated runs or subjective ratings, the top-two ordering is not statistically meaningful, and the statement that these methods 'set a new standard for the state-of-the-art' is too strong.
minor comments (4)
  1. [Section 3.1, item 3] The text states that 'This year's winning team, SRC-B' adopted a three-stage pipeline, but Table 1 lists AllForFace as the winner and no team named SRC-B appears anywhere in the paper; this appears to be an internal inconsistency that must be corrected.
  2. [Equation (3)] The ID loss is written as 1 - <R(IOut) - R(I)>, which is not a valid cosine-similarity expression; the angle brackets are undefined, and the argument should be the cosine similarity between the two embeddings.
  3. [Table 1] The table header appears to concatenate several column names ('Adaface Score Failed images ID Validation Total Score') and is hard to parse; please split the columns and clarify what 'ID Validation' means.
  4. [References [51] and [54]] References [51] and [54] appear to be the same paper by Sun et al. (PiSA-SR) with different venues and years; please cite the published version consistently and avoid duplication.

Circularity Check

2 steps flagged · score 4.0 of 10

Partial circularity: the final ranking is computed from a weighted IQA score that includes CLIPIQA and MUSIQ, and the top teams explicitly optimize those exact metrics during training or inference, so the claimed SOTA advance is partly self-confirmation.

  1. fitted input called prediction [Section 2.2.2 (Evaluation Procedure) and Section 3.3 (Fairness)]
    "The teams are ultimately ranked based on the overall perceptual score, which is computed by Score = CLIPIQA + MANIQA + MUSIQ/100 + QALIGN/5 + max(0,10−NIQE/10) + max(0,100−FID/100). ... (3) The application of no-reference IQA and simulated degradation pipelines during training and testing is considered a fair practice."

    The ranking metric is the weighted sum of CLIPIQA, MANIQA, MUSIQ, Q-Align, NIQE, and FID. The rules explicitly permit no-reference IQA during training and testing, and the method sections show the top teams used exactly those components as optimization targets: PISA-MAP runs MAP estimation in diffusion latents guided by MUSIQ and CLIP-IQA, MiPortrait uses CLIP-IQA and MUSIQ thresholds to activate GFPGAN, and AIIA trains with a CLIP-IQA loss. Because these metrics appear in the Score formula that defines the ranking, the conclusion that the top entries 'advance the performance of real-world face restoration' is partly a statement about how well the entrants optimized the ranking function itself, not an independently calibrated perceptual result.

  2. self definitional [Abstract and Section 3.2 (Participants)]
    "This collaborative effort advances the performance of real-world face restoration while offering an in-depth overview of the latest trends in the field. ... These entries set a new standard for the state-of-the-art in face restoration."

    'Performance' is never measured independently of the Score formula in Section 2.2.2; it is defined by that formula. The statement that the challenge 'advances performance' and 'sets a new standard' therefore asserts, by construction, that higher values of the organizers' chosen weighted IQA composite correspond to better face restoration. The claim is not backed by any human perceptual study or calibrated validation against ground-truth quality judgments, so the advance is a restatement of the evaluation definition rather than an externally established fact.

full rationale

This is a challenge report rather than a method derivation, so there is no equation-level circularity in the individual restoration pipelines. The main circularity concern is the evaluation loop: the final ranking is defined by a weighted sum of no-reference IQA metrics, and the challenge rules explicitly permit no-reference IQA during training and testing. Several top teams did exactly that: PISA-MAP guides latent-space MAP estimation with MUSIQ and CLIP-IQA, MiPortrait uses CLIP-IQA and MUSIQ as activation thresholds, and AIIA uses a CLIP-IQA loss. Thus the reported ordering and the claim that it 'advances the state of the art' are partly an artifact of optimizing the same composite metric used for scoring. The 450 held-out test images and the AdaFace identity gate provide genuine independent constraints, and not every score component (e.g., MANIQA, Q-Align, NIQE, FID) is optimized by all teams, so the circularity is only partial. The absence of any human baseline or calibration makes it impossible to separate perceptual improvement from metric overfitting; the 0.0077 gap between the top two teams is far smaller than the plausible effect of optimizing any single score component. I therefore assign 4/10 rather than a higher score, because the central conclusion is partially forced by the metric, but the challenge still contains independent held-out evaluation elements.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The report introduces no new theoretical entities; its assumptions are about measurement validity and dataset representativeness. The central ranking depends on several hand-chosen coefficients and thresholds and on the assumed correlation between automated scores and human judgment.

free parameters (3)
  • Score formula weighting coefficients = CLIPIQA 1, MANIQA 1, MUSIQ 0.01, Q-Align 0.2, NIQE 0.1, FID 0.01
    Hand-chosen weights in Section 2.2.2 determine the final ranking; no calibration or justification is provided.
  • AdaFace identity thresholds per dataset = 0.3 (WIDER, WebPhoto), 0.6 (LFW, CelebChild), 0.5 (CelebA)
    Set in Section 2.2.2 Step 1 without stated derivation; thresholds control which submissions are valid and thereby the final ranking.
  • Low-similarity allowance = 10 failed images
    Teams may have no more than 10 low-similarity cases; this cutoff is arbitrary and affects whether a submission gets ranked.
assumptions (4)
  • domain assumption No-reference IQA metrics (NIQE, CLIPIQA, MANIQA, MUSIQ, Q-Align) and FID correlate with human perceptual quality for restored faces.
    The entire ranking in Section 2.2.2 and conclusions in Section 3.4 rely on this; no human subjective study is presented.
  • domain assumption AdaFace cosine similarity between the low-quality input and the restored output is a valid identity-preservation check.
    Section 2.2.2 Step 1; degraded low-quality faces may not contain reliable identity features, so the reference embedding itself is uncertain.
  • domain assumption The five test subsets and 450 images are representative of real-world face restoration.
    Section 2.1; no statistical power or coverage analysis is reported.
  • domain assumption Simulated degradations applied to FFHQ images produce training pairs representative of real-world low-quality inputs.
    Section 2.2.1 recommends this practice; if the synthetic degradation model is unrepresentative, the methods' performance on real test images is less meaningful.

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Cite this review

Pith. "Pith review of NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results." pith.science (2026). https://pith.science/paper/UELKCLNC

@misc{pith2026250414600,
  author       = {Pith},
  title        = {Pith review of: NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UELKCLNC}},
  note         = {Machine review of arXiv:2504.14600}
}
read the original abstract

This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on generating natural, realistic outputs while maintaining identity consistency. Its goal is to advance state-of-the-art solutions for perceptual quality and realism, without imposing constraints on computational resources or training data. The track of the challenge evaluates performance using a weighted image quality assessment (IQA) score and employs the AdaFace model as an identity checker. The competition attracted 141 registrants, with 13 teams submitting valid models, and ultimately, 10 teams achieved a valid score in the final ranking. This collaborative effort advances the performance of real-world face restoration while offering an in-depth overview of the latest trends in the field.

Figures

Figures reproduced from arXiv: 2504.14600 by the authors.

Figure 2
Figure 2. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 1
Figure 1. Team AllForFace. Basic idea. them into a serial pipeline to achieve high-quality recon￾structed images [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Team AllForFace. Naturalness model. based methods often fail to conform to realistic hair strand patterns. To address the second subtask, diffusion models were prioritized for consideration. Since the image has al￾ready achieved a high degree of ID consistency following the first subtask, this provides strong control information for diffusion models. Leveraging the prior knowledge em￾bedded in the diffusion process … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Team IIL. After completing the two subtasks, they obtained facial images with improved texture details. However, upon ob￾servation, it became evident that some of the restored faces did not align with the statistical distribution characteristics of natural images. For …
Figure 6
Figure 6. Figure 6: Team MiPortrait. The training process of their SDFace uses the AdamW optimizer with β1 = 0.5 and β2 = 0.999, with a learning rate of 1 × 10−4 . All experiments were conducted on a server equipped with two NVIDIA A6000 GPUs. The model uses pretrained SDXL-Turbo [45], an…
Figure 7
Figure 7. Figure 7: Team AIIA. gate the whitening effect. At the end of the entire process, a skin-tone-specific UNet is utilized. This application aims to render the face more natural-looking and ensure it bet￾ter aligns with their aesthetic criteria. The workflow of the proposed algorit…

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.