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
Latest papers with no code
FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting
Blind face inpainting refers to the task of reconstructing visual contents without explicitly indicating the corrupted regions in a face image.
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains
In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains.
Pixel Sampling for Style Preserving Face Pose Editing
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc.
Extreme Face Inpainting with Sketch-Guided Conditional GAN
Recovering badly damaged face images is a useful yet challenging task, especially in extreme cases where the masked or damaged region is very large.
Foreground-guided Facial Inpainting with Fidelity Preservation
It introduces the use of foreground segmentation masks to preserve the fidelity.
Learning Oracle Attention for High-fidelity Face Completion
While recent works adopted the attention mechanism to learn the contextual relations among elements of the face, they have largely overlooked the disastrous impacts of inaccurate attention scores; in addition, they fail to pay sufficient attention to key facial components, the completion results of which largely determine the authenticity of a face image.
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning
This paper proposes a multi-scale feature graph generative adversarial network (MFG-GAN) to implement the face restoration of images in which both degradation modes coexist, and also to repair images with a single type of degradation.
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents.
Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs
The proposed frequency-oriented attentive module (FOAM) encourages GANs to attend to only finer details in the coarse-to-fine progressive training, thus enabling progressive attention to face structures.
Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition
This paper models high resolution heterogeneous face synthesis as a complementary combination of two components, a texture inpainting component and pose correction component.
