Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further
closed the gap between image quality and human perceptual preferences. They are easy to …

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 …

This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a
pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task …

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …

We present DiffBIR, a general restoration pipeline that could handle different blind image
restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem …

Diffusion models have demonstrated impressive performance in various image generation,
editing, enhancement and translation tasks. In particular, the pre-trained text-to-image stable …

Speech-driven 3D facial animation has been widely studied, yet there is still a gap to
achieving realism and vividness due to the highly ill-posed nature and scarcity of audio …

Existing dehazing approaches struggle to process real-world hazy images owing to the lack
of paired real data and robust priors. In this work, we present a new paradigm for real image …

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 …