Title: SPIDER: Boosting Blind Face Restoration via Simultaneous Prior Injection and Degradation Removal
Venue: NeurIPS 2025
Accepted: no
Keywords: Blind Face Restoration

Super-resolution

Degradation Removal

Diffusion Priors
Abstract: Existing blind face restoration (BFR) methods suffer from drastic performance drop under severe degradations. A common strategy is to first remove degradations and then restore the face by fully harnessing generative models. However, this sequential pipeline risks discarding subtle but crucial cues from already limited low-quality (LQ) inputs. To address this, we introduce a new learning paradigm: simultaneous prior injection and degradation removal (SPIDER). Unlike prior approaches, SPIDER injects semantic priors before degradation removal, thereby preserving identity-relevant features and mitigating the impact of corrupted LQ features. SPIDER consists of two key modules: (1) a prior injection module that distills purified degradation-unaware semantic control tokens from vision-language models,  and (2) a degradation removal module equipped with an image-to-text degradation mapper and a degradation remover that refines distorted features into robust representations. This design leads to boosted BFR performance. Extensive experiments on both synthetic and real-world datasets, including challenging surveillance scenarios, demonstrate SPIDER's clear superiority over state-of-the-art BFR methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nExisting blind face restoration methods perform poorly under severe degradations, SPIDER is proposed to inject semantic prior before degradation removal, comprising a prior injection module and a degradation removal module. Experiments on synthetic and real-world datasets show SPIDER outperforms state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Paper strength**\n\n1. The paper has a very clear writing framework, logically presenting the research from problem to solution and validation. \n2. SPIDER achieves SOTA results in face restoration, especially under severe degradations, outperforming others on synthetic and real-world datasets. \n3. The authors point out the flaws of existing methods on the new SCFace dataset, showing their poor performance in extreme surveillance conditions.\n\n**Paper weakness**\n\n1. The primary issue is the failure to clearly explain why simultaneous degradation removal and prior injection are superior for the restoration task. \n2. A secondary problem is that performing subsequent conditional generation after the DRM module still seems to follow the sequence of degradation removal before prior injection. \n3. Mapping images to a high-level semantic space using CLIP at the outset does not make much sense—since the textual space is too abstract, how are low-level details preserved? \n4. The methodology section does not highlight the method’s unique features, instead focusing on describing previous diffusion training approaches. \n5. Ablation studies indicate that prompt design is crucial, yet specific designs are not provided in the main text. \n6. The significance of Section 4.4 is unclear, as similar t-SNE visualizations could be conducted for any degradation removal method.\n\nQUESTIONS:\n1. Is using only 30 learnable tokens to represent degradation-removed images causing an excessively high compression ratio, which might extract high-level semantics but significantly reduce the restoration ceiling when degradations are less severe? \n2. Intuitively, emphasizing high-level semantics should yield better performance on perceptual metrics, yet experimental results show superior performance on fidelity metrics like PSNR. What accounts for this discrepancy? \n3. The order of Sections 3.2 and 3.3 can be swapped to follow the sequence of Stage 1 first and then Stage 2.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SPIDER, an interesting paradigm for blind face restoration that interleaves multi-level prior injection with degradation removal within a unified architecture. The core idea is to inject semantic priors from vision-language models before applying degradation removal, thereby guiding the restoration process with rich semantic information and improving the robustness of the model under severe degradations. The authors leverage diffusion models to encode and suppress degradation, leading to better preservation of facial identity and finer details, which are validated through extensive experiments on synthetic and real-world datasets, including surveillance imagery. The approach demonstrates superior quantitative performance and perceptual metrics, alongside qualitative results that show clear improvements over prior state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.\tThe key contribution of injecting semantic priors prior to degradation removal is well-motivated and convincingly demonstrated through both qualitative and quantitative analyses. \n2.\tThe interleaved architecture effectively balances semantic consistency with degradation suppression.\n3.\tThe ablation studies, feature visualizations using t-SNE, and detailed experimental setups bolster the credibility of the proposed approach. The insights into the degradation removal mechanism and the importance of prompt design further deepen the technical contribution.\n\nWeaknesses:\n1.\tThe idea of semantic prior injection echoes recent trends in integrating vision-language models for image restoration. The architecture primarily combines existing diffusion models and prior injection strategies without much novelty in the face restoration methodology. The actual architectural and algorithmic innovations are somewhat incremental and lack a substantial leap beyond existing works.\n2.\tThe visual results presented in the manuscript are impressive; however, the reported metrics do not reach SOTA performance. Please clarify the underlying reasons for this discrepancy, as quantitative metrics reflect overall performance, whereas visual results may only highlight selective strengths.\n3.\tThe robustness to different types of degradations, real-world variances, or potential failure cases where prior injection might introduce artifacts is underexplored.\n4.\tTo ensure fair evaluation, please consider adding image or face restoration methods based on language models or prompt learning, especially those proposed in the past two years.\n5.\tThe manuscript does not clearly specify which degradations are being removed. The ablation study also lacks depth. Please consider providing explicit visual results instead of abstract, implicit descriptions to clarify the effectiveness of each component.\n6.\tThe introduction of numerous modules and extensive tuning may reduce the model's efficiency. Please discuss and compare the model complexity explicitly.\n\nQUESTIONS:\nPlease refer to the weakness!\n\nLIMITATIONS:\n1.\tThe idea of semantic prior injection echoes recent trends in integrating vision-language models for image restoration. The architecture primarily combines existing diffusion models and prior injection strategies without much novelty in the face restoration methodology. The actual architectural and algorithmic innovations are somewhat incremental and lack a substantial leap beyond existing works.\n2.\tThe visual results presented in the manuscript are impressive; however, the reported metrics do not reach SOTA performance. Please clarify the underlying reasons for this discrepancy, as quantitative metrics reflect overall performance, whereas visual results may only highlight selective strengths.\n3.\tThe robustness to different types of degradations, real-world variances, or potential failure cases where prior injection might introduce artifacts is underexplored.\n4.\tTo ensure fair evaluation, please consider adding image or face restoration methods based on language models or prompt learning, especially those proposed in the past two years.\n5.\tThe manuscript does not clearly specify which degradations are being removed. The ablation study also lacks depth. Please consider providing explicit visual results instead of abstract, implicit descriptions to clarify the effectiveness of each component.\n6.\tThe introduction of numerous modules and extensive tuning may reduce the model's efficiency. Please discuss and compare the model complexity explicitly.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes SPIDER for blind face restoration task. SPIDER addresses severe degradations by simultaneously injecting semantic prior and removing degradation, which utilizes a prior injection module to extract semantic tokens from vision-language models and a degradation removal module to project noisy features into a textual space for robust denoising. Various experiments on synthetic and real-world datasets testify the effectiveness of SPIDER.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The SPIDER framework is novel, effectively enhancing blind face restoration performance under severe degradations by simultaneously injecting semantic priors and removing degradations. \n2. The decoupled cross-attention mechanism achieve collaborative optimization of semantic guidance and denoising. \n3. Various comprehensive experiments shows that SPIDER outperforms existing methods in extreme degradation scenarios, verifying its effectiveness. \n\nWeaknesses:\n1. The feature convergence in the text space is only proven by t-SNE visualization, but there is no theoretical proof or experimental comparison showing that the noise robustness of the text embedding space is superior to other spaces.  \n2. The article has a relatively strong technical inheritance from existing methods. For example, the extraction of semantic priors relies on LLaVA, and degradation removal depends on CLIP mapping. The paper does not clearly explain the innovative points of SPIDER compared to existing methods, which may limit the actual effect of the model in complex scenarios.\n\nQUESTIONS:\n1. I wonder has the paper tested whether the model can maintain a relatively high level of image restoration capability as the degree of degradation increases. \n2. In the blind face restoration task, why does the paper choose the text embedding space to achieve guided denoising? Has it compared the denoising effects of other image feature spaces?\nIf the proposed questions can be effectively explained and solved, I will consider improving my score.\n\nLIMITATIONS:\nThe article does not introduce the limitations of the proposed model.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SPIDER, a new blind face restoration framework that proposes a learning paradigm: simultaneous semantic prior injection and degradation removal. Rather than sequentially removing degradations and then applying generative priors, SPIDER injects a vision-language prior before degradation removal, in an effort to better retain facial semantics under severe noise. The system comprises a Degradation Removal Module (DRM) and a Prior Injection Module, integrated via a decoupled cross-attention mechanism. The authors report improvements over recent SOTA methods on both synthetic and real-world datasets.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n\n+ The simultaneous use of semantic priors and degradation removal is different from the traditional sequential pipeline.\n\n+ The method is tested on both synthetic and multiple real-world datasets, which simulate extreme surveillance degradation. The method is shown to outperform or match state-of-the-art methods such as FaithDiff and DAEFR on several benchmarks.\n\n+ The ablation studies support the paper's core claims and explore design choices like module ordering and prompt style.\n\n**Weakness**\n\n- Overall, the DRM module essentially performs cross-modal mapping using CLIP features and basic projections, which is conceptually straightforward and does not introduce fundamentally new degradation modeling techniques. Degradation removal as a strategy has been extensively explored, and the design here does not break new ground in that regard.\n\n- The paper repeatedly emphasizes the effectiveness of DRM, yet its results are more like the personalization using an identity extractor (like arc2face) to generate the person image of the same identity (see Figures 10, 11, 12 in suppl). The use of the degradation mapper seems not very necessary. We can just use the degradation remover module without introducing the degradation mapper. Such a design is similar to these using SwinIR to get the degradation removal results as the condition of diffusion models.\n\n- While the authors include some ablations (e.g., with/without DRM), they omit key comparisons, such as:\n\n  - Using strong standalone degradation removal backbones (e.g., SwinIR) followed by face restoration.\n\n  - Using different text prompts for the same LR face image (with different degradation levels). In this case, I also wonder whether the LLAVA can obtain an accurate description of the severely degraded LR input and its effect on the restoration results.\n\nThese are important to isolate the contribution of the new paradigm.\n\n- The related work section insufficiently discusses recent BFR models, particularly diffusion-based approaches like DifFace, or GAN-based frameworks such as GPEN, etc. Many related works are not discussed. These omissions make it difficult to appreciate where SPIDER stands among the broader BFR literature. It is better to refer to a blind face restoration survey to have a deep discussion of this task.\n\n\n- The vision-language prior is obtained via LLaVA, which is also widely adopted in natural image restoration, and the work does not significantly expand or innovate upon this process beyond prompt engineering. We can also use a fixed prompt during the training and inference phase. It is better to compare it. As such, the “semantic prior” component may be overstated in novelty.\n\n\nWhile the paper presents a well-structured and reasonably well-pipeline, its core contributions are incremental in nature, combining existing ideas (text priors, degradation removal, cross-attention fusion) in a sensible but not deeply innovative way. The experimental section would benefit from deeper analysis and stronger baselines in the ablation study. Additionally, missing discussions of relevant prior work limit the paper’s contextual completeness.\n\nQUESTIONS:\nSee weakness\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}