Facial Inpainting

19 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 )

Libraries

Use these libraries to find Facial Inpainting models and implementations

Most implemented papers

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.

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.

Generative Face Completion

Yijunmaverick/GenerativeFaceCompletion CVPR 2017

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

Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control

wuyangluo/reffaceinpainting 13 Mar 2023

To introduce strong control for face inpainting, we propose a novel reference-guided face inpainting method that fills the large-scale missing region with identity and texture control guided by a reference face image.

PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting

humansensinglab/PATMAT ICCV 2023

By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity.

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.

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.

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.

FSGAN: Subject Agnostic Face Swapping and Reenactment

YuvalNirkin/fsgan ICCV 2019

We present Face Swapping GAN (FSGAN) for face swapping and reenactment.

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.