95 citations · 281 across the 21 of their papers we have counts for
20 papers · 1 filter
ProPainter: Improving Propagation and Transformer for Video Inpainting
Shangchen Zhou, Chongyi Li, Kelvin C. K. Chan +1
Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms in video inpainting (VI). Despite the effectiveness of these components, they still suffer from…
Dual Associated Encoder for Face Restoration
Yu-Ju Tsai, Yu-Lun Liu, Lu Qi +2
Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild. The existing codebook…
Exploiting Diffusion Prior for Real-World Image Super-Resolution
Jianyi Wang, Zongsheng Yue, Shangchen Zhou +2
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our t…
Collaborative Diffusion for Multi-Modal Face Generation and Editing
Ziqi Huang, Kelvin C. K. Chan, Yuming Jiang +1
Diffusion models arise as a powerful generative tool recently. Despite the great progress, existing diffusion models mainly focus on uni-modal control, i.e., the diffusion process…
Identity Encoder for Personalized Diffusion
Yu-Chuan Su, Kelvin C. K. Chan, Yandong Li +5
Many applications can benefit from personalized image generation models, including image enhancement, video conferences, just to name a few. Existing works achieved personalization…
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models
Xuhui Jia, Yang Zhao, Kelvin C. K. Chan +6
This paper proposes a method for generating images of customized objects specified by users. The method is based on a general framework that bypasses the lengthy optimization requi…
ReVersion: Diffusion-Based Relation Inversion from Images
Ziqi Huang, Tianxing Wu, Yuming Jiang +2
Diffusion models gain increasing popularity for their generative capabilities. Recently, there have been surging needs to generate customized images by inverting diffusion models f…
Reference-based Image and Video Super-Resolution via C2-Matching
Yuming Jiang, Kelvin C. K. Chan, Xintao Wang +2
Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image or video by introducing an additional high-resol…
GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
Kelvin C. K. Chan, Xiangyu Xu, Xintao Wang +2
We show that pre-trained Generative Adversarial Networks (GANs) such as StyleGAN and BigGAN can be used as a latent bank to improve the performance of image super-resolution. While…
Exploring CLIP for Assessing the Look and Feel of Images
Jianyi Wang, Kelvin C. K. Chan, Chen Change Loy
Measuring the perception of visual content is a long-standing problem in computer vision. Many mathematical models have been developed to evaluate the look or quality of an image.…
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
Shangchen Zhou, Kelvin C. K. Chan, Chongyi Li +1
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…
On the Generalization of BasicVSR++ to Video Deblurring and Denoising
Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu +1
The exploitation of long-term information has been a long-standing problem in video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in video super…
Investigating Tradeoffs in Real-World Video Super-Resolution
Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu +1
The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation le…
Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning
Yihao Liu, Hengyuan Zhao, Kelvin C. K. Chan +4
Video colorization is a challenging and highly ill-posed problem. Although recent years have witnessed remarkable progress in single image colorization, there is relatively less re…
Robust Reference-based Super-Resolution via C2-Matching
Yuming Jiang, Kelvin C. K. Chan, Xintao Wang +2
Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR…
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu +1
A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature align…
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
Kelvin C. K. Chan, Xintao Wang, Ke Yu +2
Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not…
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
Kelvin C. K. Chan, Xintao Wang, Xiangyu Xu +2
We show that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolut…
Understanding Deformable Alignment in Video Super-Resolution
Kelvin C. K. Chan, Xintao Wang, Ke Yu +2
Deformable convolution, originally proposed for the adaptation to geometric variations of objects, has recently shown compelling performance in aligning multiple frames and is incr…
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Xintao Wang, Kelvin C. K. Chan, Ke Yu +2
Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is relea…