Liying Lu

We generate realistic low-light noise images through a specially designed diffusion model. We analyze the characteristics of the generated noise, and show the denoising results trained with our synthetic data.

Combines structure-accurate 3D priors and texture-rich 2D priors in pretrained generative networks for blind face restoration under extreme conditions

We leverage in-the-wild 2D talking-head videos to train a 3D facial animation model.

By relaxing the immutable one-to-one constraint of the single image super-resolution task, we allow the estimated patches to dynamically seek the best supervision during training.

This paper addresses the task of video frame interpolation, leveraging Transformers to model long-range pixel correlations across video frames.

We present a generic iterative alignment module which employs a gradual refinement scheme for sub-alignments of video restoration.

An approach for recovering high-frequency details in low-quality images by utilizing external reference images. We accelerate the correspondence matching process and introduce an improved feature adaptation method.

A multi-scale architecture used to complete the sparse depth map, with a knowledge distillation method employed to achieve high performance and fast speed.