Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing
the resolution of low-resolution (LR) face images to generate high-resolution face images, is …

Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to
1) improve the map** from degraded inputs to desired outputs, or 2) complement high …

We present a novel high-fidelity generative adversarial network (GAN) inversion framework
that enables attribute editing with image-specific details well-preserved (eg, background …

Blind face restoration usually relies on facial priors, such as facial geometry prior or
reference prior, to restore realistic and faithful details. However, very low-quality inputs …

Although generative facial prior and geometric prior have recently demonstrated high-quality
results for blind face restoration, producing fine-grained facial details faithful to inputs …

Blind face restoration usually synthesizes degraded low-quality data with a pre-defined
degradation model for training, while more complex cases could happen in the real world …

While deep learning-based methods for blind face restoration have achieved
unprecedented success, they still suffer from two major limitations. First, most of them …

Blind face restoration is to recover a high-quality face image from unknown degradations. As
face image contains abundant contextual information, we propose a method …

Face image super resolution (face hallucination) usually relies on facial priors to restore
realistic details and preserve identity information. Recent advances can achieve impressive …