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 )
Libraries
Use these libraries to find Facial Inpainting models and implementationsLatest papers
E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space
We further improve the StyleGAN output to find the optimal code in the latent space using a new optimization for GAN inversion technique.
Compensation Sampling for Improved Convergence in Diffusion Models
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.
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Based on this disentanglement, face swapping can be simplified as style and mask swapping.
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
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.
Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control
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.
Reference Guided Image Inpainting using Facial Attributes
Image inpainting is a technique of completing missing pixels such as occluded region restoration, distracting objects removal, and facial completion.
SFI-Swin: Symmetric Face Inpainting with Swin Transformer by Distinctly Learning Face Components Distributions
None of the powerful existing models can fill out the missing parts of an image while considering the symmetry and homogeneity of the picture.
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.
Masked Face Inpainting Through Residual Attention UNet
Realistic image restoration with high texture areas such as removing face masks is challenging.
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.