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
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
run-youngjoo/SC-FEGAN
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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
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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
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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
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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
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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.
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
e4s2024/e4s2024
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Based on this disentanglement, face swapping can be simplified as style and mask swapping.
Learning Symmetry Consistent Deep CNNs for Face Completion
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
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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?
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
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ICCV 2019
We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
