Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input
images, which is a domain-specific image restoration problem in the low-level computer …

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

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 …

We address the problem of learning person-specific facial priors from a small number (eg,
20) of portrait photos of the same person. This enables us to edit this specific person's facial …

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the
low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved …

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 text image super-resolution (SR) is challenging as one needs to cope with diverse font
styles and unknown degradation. To address the problem, existing methods perform …

Generating a talking face video from the input audio sequence is a practical yet challenging
task. Most existing methods either fail to capture fine facial details or need to train a specific …

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods
have achieved great progress in restoring degraded facial details by joint training with facial …

Exploiting pre-trained diffusion models for restoration has recently become a favored
alternative to the traditional task-specific training approach. Previous works have achieved …