Facial Inpainting
21 papers with code • 3 benchmarks • 4 datasets
Facial inpainting (or face completion) is the task of generating plausible facial structures for missing pixels in a face image.
( Image credit: SymmFCNet )
Benchmarks
These leaderboards are used to track progress in Facial Inpainting
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Libraries
Use these libraries to find Facial Inpainting models and implementations
FFHQ
VGGFace2
CASIA-WebFace
DREAMING Inpainting Dataset
Most implemented papers
FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
Combined variations containing low-resolution and occlusion often present in face images in the wild, e. g., under the scenario of video surveillance.
LaFIn: Generative Landmark Guided Face Inpainting
YaN9-Y/lafin
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It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions.
Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars
We introduce a novel attribute similarity metric to encourage networks to learn the style of facial attributes from the exemplar in a self-supervised way.
Non-Deterministic Face Mask Removal Based On 3D Priors
This paper presents a novel image inpainting framework for face mask removal.
HiMFR: A Hybrid Masked Face Recognition Through Face Inpainting
Inspired by the recent image inpainting methods, we propose an end-to-end hybrid masked face recognition system, namely HiMFR, consisting of three significant parts: masked face detector, face inpainting, and face recognition.
Masked Face Inpainting Through Residual Attention UNet
mdhosen/mask-face-inpainting-using-residual-attention-unet
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Realistic image restoration with high texture areas such as removing face masks is challenging.
Conffusion: Confidence Intervals for Diffusion Models
Diffusion models have become the go-to method for many generative tasks, particularly for image-to-image generation tasks such as super-resolution and inpainting.
SFI-Swin: Symmetric Face Inpainting with Swin Transformer by Distinctly Learning Face Components Distributions
mohammadrezanaderi4/sfi-swin
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None of the powerful existing models can fill out the missing parts of an image while considering the symmetry and homogeneity of the picture.
Reference Guided Image Inpainting using Facial Attributes
Stillrot/RGINP
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Image inpainting is a technique of completing missing pixels such as occluded region restoration, distracting objects removal, and facial completion.
Compensation Sampling for Improved Convergence in Diffusion Models
hotfinda/Compensation-sampling
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We argue that the denoising process is crucially limited by an accumulation of the reconstruction error due to an initial inaccurate reconstruction of the target data.
