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HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
Lingbo Yang, Chang Liu, Pan Wang, Shanshe Wang, Peiran Ren, Siwei Ma, Wen Gao
TL;DR
Existing face restoration methods often depend on degradation priors or explicit structural guidance, limiting generalization to heterogeneous real-world images. HiFaceGAN formulates dual-blind Face Renovation as semantic-guided generation with nested collaborative suppression and replenishment units. Experiments on synthetic and real images report strong versatility, robustness, and generalization across restoration tasks, while identifying challenging boundaries for regular geometric objects and occluded faces.
Problem
Existing face restoration methods rely on degradation assumptions or explicit guidance, limiting generalization to real images with heterogeneous degradation and rich contents.
Method
HiFaceGAN formulates dual-blind Face Renovation as semantic-guided generation using nested collaborative suppression and replenishment units with hierarchical semantic guidance.
Results
Experiments on synthetic and real face images show HiFaceGAN's versatility, robustness, and generalization across challenging restoration tasks, with semantic-aware residuals and strong perceptual performance.
Takeaways & Limitations
The results support dual-blind generative face renovation as a broad approach to real-world restoration without degradation or structural priors.
Takeaways & Limitations
Regular geometric objects such as glasses and partially occluded faces remain challenging, where external structural guidance may help.
Abstract
from arXiv · showhide
Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with heterogeneous degradations and rich background contents. In this paper, we investigate the more challenging and practical "dual-blind" version of the problem by lifting the requirements on both types of prior, termed as "Face Renovation"(FR). Specifically, we formulated FR as a semantic-guided generation problem and tackle it with a collaborative suppression and replenishment (CSR) approach. This leads to HiFaceGAN, a multi-stage framework containing several nested CSR units that progressively replenish facial details based on the hierarchical semantic guidance extracted from the front-end content-adaptive suppression modules. Extensive experiments on both synthetic and real face images have verified the superior performance of HiFaceGAN over a wide range of challenging restoration subtasks, demonstrating its versatility, robustness and generalization ability towards real-world face processing applications.
1 INTRODUCTION
Existing face restoration methods struggle with heterogeneous real-world degradations because they typically assume specific degradation or external structural guidance. HiFaceGAN introduces dual-blind Face Renovation using collaborative suppression and replenishment, with experiments showing broad advantages over existing approaches.
- Motivation: Face restoration methods designed for prescribed degradations generalize poorly to real images containing complex, heterogeneous degradation.Tasks include super-resolution, hallucination, denoising, deblurring, and compression artifact removal.
- Motivation: Blind restoration without degradation priors remains difficult because faithful detail reconstruction often requires categorical or structural guidance.Existing blind face methods use guidance such as landmarks, parsing maps, and component heatmaps.
- Contribution: Face Renovation defines photo-realistic restoration under a dual-blind condition, removing requirements for both degradation and structural priors during training.The task targets unconstrained face restoration in real-world settings.
- Contribution: HiFaceGAN uses nested collaborative suppression and replenishment units to progressively restore facial details from hierarchical semantic guidance.Content-adaptive suppression extracts layered semantic features that guide replenishment of corresponding contents.
- Results: Experiments on synthetic and real face images show HiFaceGAN outperforming varied non-blind and single-blind baselines in versatility, robustness, and generalization.Figure 1 reports the best perceptual quality by NIQE among related state-of-the-art methods.
2 RELATED WORKS
Related restoration methods commonly prescribe degradation conditions or rely on external facial guidance, limiting unconstrained restoration. The paper positions face renovation as a generative alternative for real-world restoration without those requirements.
- Restoration Methods: Non-blind restoration methods prescribe degradation type and intensity, producing specialized subtasks with limited generalization to complex real images.The related subtasks include denoising, deblurring, compression artifact removal, super-resolution, and face hallucination.
- Blind Face Restoration: Blind restoration methods still show sensitivity to degradation profiles, while face-specific approaches commonly use semantic, identity, landmark, or component guidance.These forms of external guidance are used to improve restoration performance.
- Face Renovation: The proposed dual-blind face renovation framework targets unconstrained real-world restoration and can outperform state-of-the-art single-blind methods.The paper attributes this result to collaborative suppression and replenishment and reports improved enhancement of non-facial contents.
- Generative Face Models: Generative face methods include semantic-guided approaches that control layout or expression and style-guided approaches that inject style through adaptive normalization.The paper frames its approach within conditional high-resolution face generation.
3 FACE RENOVATION
Face Renovation addresses unconstrained images with unidentified heterogeneous degradation by combining degradation suppression and content replenishment. Its evaluation uses synthetic degradations and metrics spanning statistical fidelity, semantic fidelity, and perceptual naturalness.
- Degradation Simulation: The synthetic FFHQ benchmark combines multiple degradation types in random order with uniformly sampled intensity to simulate real-world restoration conditions.The full Face Renovation degradation is more complex than individual subtasks, while 16x mosaic is the most severe listed corruption.
- Evaluation Criterion: The degradation simulation and evaluation design cover both restoration versatility and perceptual realism in challenging real-world settings.The supplied implementation note indicates the degradation-generation script is provided in supplementary materials.
- Face Renovation: Real-world face renovation requires a unified solution that handles unidentified heterogeneous degradation without prior knowledge.The task can be decomposed into suppressing degradation effects and replenishing lost content.
- Evaluation Criterion: Evaluation combines PSNR and SSIM with semantic fidelity measures and NIQE to better reflect human perception of renovated faces.FED and LLE measure semantic fidelity, while NIQE measures naturalness for in-the-wild face images.
- Degradation Simulation: Figure 2 visualizes degradation types alongside their corresponding face manipulation tasks.It provides the task-oriented overview of the simulated degradation settings.
4 THE PROPOSED HIFACEGAN
HiFaceGAN combines content-adaptive suppression with semantic-guided replenishment in nested, multi-stage CSR units to renovate faces under heterogeneous degradation. Its progressive stages suppress artifacts, recover hierarchical details, and produce semantic-aware improvements across faces and backgrounds.
- 4.1 Network Architecture: HiFaceGAN uses cascaded suppression and replenishment modules to extract layered semantics and progressively reconstruct corresponding facial details.Suppression modules filter heterogeneous degradation while replenishment modules use the resulting semantic hierarchy.
- 4.1 Network Architecture: The nested architecture assigns each CSR unit a semantic aspect, with cascaded replenishment modules attending to successive semantic layers.This design implements a multi-stage renovation pipeline aligned with the hierarchy extracted by front-end suppression.
- 4.1 Network Architecture: Content-adaptive suppression replaces fixed convolution to select informative responses and filter degradation artifacts using neighborhood-feature correlations.The modulation function adjusts kernel weights according to correlations between neighboring features.
- 4.2 Loss Functions: Adversarial, multi-scale feature-matching, and perceptual losses jointly target semantic fidelity, facial-detail quality, and visual realism.The final reconstruction objective combines the adversarial loss with weighted feature-matching and perceptual losses.
- 4.3 Discussion: Progressive renovation overlays stage-specific contents from inner to outer semantics, gradually increasing resolution and visual realism.The controlled visualization supports the architectural rationale and interpretability of the multi-stage process.
- 4.3 Discussion: Compared with GFRNet, HiFaceGAN produces semantic-aware residuals by jointly suppressing artifacts and replenishing details, while improving foreground faces and backgrounds together.The comparison attributes GFRNet’s noisier residuals to PSNR-oriented optimization and insufficient replenishment.
5 EXPERIMENTS
HiFaceGAN is evaluated across five restoration subtasks, synthetic and real images, and comparisons with task-specific and blind baselines. It achieves strong perceptual and qualitative results, while ablations examine semantic guidance, nested architecture, suppression, and statistical–perceptual trade-offs.
- Comparison with Task-Specific Baselines: 3-10 times gain on FID and 50%-200% gain on LPIPS over all baselines establishes HiFaceGAN’s strongest quantitative advantage in perceptual performance.The comparison covers face renovation and five related tasks in Table 1.
- Qualitative Comparison: HiFaceGAN replenishes convincing facial details across all subtasks, whereas competing methods produce over-smoothed, color-shifted, or artifact-laden results.Reported details include hair bangs, beards, wrinkles, and non-facial microphone grids.
- Dual-Blind vs. Single-Blind: Even with high-quality guidance, GFRNet is less effective than HiFaceGAN at suppressing noise and replenishing fine-grained details, while its gains remain mainly statistical.The comparison supports the paper’s claim that explicit guidance is not necessary for strong face renovation.
- Historic Photograph Renovation: On the historic Solvay Conference photograph, HiFaceGAN produces higher-definition, higher-fidelity portraits with fewer artifacts and realistic facial details than competing methods.Minor artifacts remain mostly in shading regions where degradation severely corrupted the underlying contents.
- Ablation Study: Ablations show that semantic features, nested multi-stage renovation, and content-adaptive suppression each improve performance, while L1 shifts results toward PSNR/SSIM at the expense of FID/LPIPS/NIQE.The study evaluates guidance type, architecture, and component design on 16x face hallucination.
6 CONCLUSION AND FUTURE WORK
The paper presents Face Renovation and HiFaceGAN as a dual-blind framework for real-world face restoration without degradation or structural guidance during training. Experiments support broad performance and generalization, while severe degradation, ethnic diversity, regular geometry, and occlusions remain future challenges.
- Conclusion: Face Renovation removes the training requirements for both degradation priors and structural guidance, and HiFaceGAN implements it through collaborative suppression and replenishment.The framework is intended for practical real-world photo repairing applications.
- Conclusion: Experiments on synthetic faces and historic photographs demonstrate HiFaceGAN’s versatility, robustness, and generalization across a wide range of restoration tasks.The paper reports performance above current state-of-the-art methods by a large margin.
- Future Work: Severe degradation can create content ambiguity, including misreading an Afro haircut as straight hair, motivating more diverse and balanced data collection.The authors identify this as a future direction rather than a resolved issue.
- Future Work: Renovating regular geometric objects such as glasses and partially occluded faces remains challenging, motivating multimodal generation with structural and semantic guidance.The paper identifies external structural guidance as potentially beneficial in these cases.