Blind Face Restoration
24 papers with code • 4 benchmarks • 4 datasets
Blind face restoration aims at recovering high-quality faces from the low-quality counterparts suffering from unknown degradation, such as low-resolution, noise, blur, compression artifacts, etc. When applied to real-world scenarios, it becomes more challenging, due to more complicated degradation, diverse poses and expressions.
Description source: Towards Real-World Blind Face Restoration with Generative Facial Prior
Image source: Towards Real-World Blind Face Restoration with Generative Facial Prior
Libraries
Use these libraries to find Blind Face Restoration models and implementations
CelebA
CelebA-HQ
LFW
WIDER
Most implemented papers
Implicit Subspace Prior Learning for Dual-Blind Face Restoration
Face restoration is an inherently ill-posed problem, where additional prior constraints are typically considered crucial for mitigating such pathology.
Towards Real-World Blind Face Restoration with Generative Facial Prior
TencentARC/GFPGAN
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CVPR 2021
Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details.
RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs
wzhouxiff/restoreformer
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CVPR 2022
Blind face restoration is to recover a high-quality face image from unknown degradations.
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
tencentarc/vqfr
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Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods.
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.
Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration
yyj1ang/mfpsnet
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To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.
Learning Dual Memory Dictionaries for Blind Face Restoration
Generally, it is a challenging and intractable task to improve the photo-realistic performance of blind restoration and adaptively handle the generic and specific restoration scenarios with a single unified model.
DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration
Kaldwin0106/DR2_Drgradation_Remover
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CVPR 2023
However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data.
OPHAvatars: One-shot Photo-realistic Head Avatars
And with the coarse video, our method synthesizes a coarse talking head avatar with a deforming neural radiance field.
Dual Associated Encoder for Face Restoration
LIAGM/DAEFR
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Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild.
