Facial Inpainting

9 papers with code • 3 benchmarks • 3 datasets

Facial inpainting (or face completion) is the task of generating plausible facial structures for missing pixels in a face image.

( Image credit: SymmFCNet )

Greatest papers with code

SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

run-youngjoo/SC-FEGAN ICCV 2019

We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.

Facial Inpainting

Image Fine-grained Inpainting

Zheng222/DMFN 7 Feb 2020

Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones.

Facial Inpainting Fine-Grained Image Inpainting

LaFIn: Generative Landmark Guided Face Inpainting

YaN9-Y/lafin 26 Nov 2019

It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions.

Facial Inpainting

FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning

swordcheng/FCSR-GAN 4 Nov 2019

Combined variations containing low-resolution and occlusion often present in face images in the wild, e. g., under the scenario of video surveillance.

Face Identification Facial Inpainting +2

Learning Symmetry Consistent Deep CNNs for Face Completion

csxmli2016/SymmFCNet 19 Dec 2018

As for missing pixels on both of half-faces, we present a generative reconstruction subnet together with a perceptual symmetry loss to enforce symmetry consistency of recovered structures.

Face Recognition Facial Inpainting

Does Generative Face Completion Help Face Recognition?

isi-vista/face-completion 7 Jun 2019

Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information.

Face Recognition Facial Inpainting

Generative Face Completion

easternCar/Face-Parsing-Network CVPR 2017

In this paper, we propose an effective face completion algorithm using a deep generative model.

Facial Inpainting Semantic Parsing

Detecting Overfitting of Deep Generative Networks via Latent Recovery

ryanwebster90/gen-overfitting-latent-recovery CVPR 2019

Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods.

Facial Inpainting Super-Resolution