50 citations · 154 across the 8 of their papers we have counts for
10 papers
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…
3D Human Pose, Shape and Texture from Low-Resolution Images and Videos
Xiangyu Xu, Hao Chen, Francesc Moreno-Noguer +2
3D human pose and shape estimation from monocular images has been an active research area in computer vision. Existing deep learning methods for this task rely on high-resolution i…
Exploiting Raw Images for Real-Scene Super-Resolution
Xiangyu Xu, Yongrui Ma, Wenxiu Sun +1
Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single ima…
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
Xiangyu Xu, Muchen Li, Wenxiu Sun +1
Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explici…
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…
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
Xiangyu Xu, Hao Chen, Francesc Moreno-Noguer +2
3D human shape and pose estimation from monocular images has been an active area of research in computer vision, having a substantial impact on the development of new applications,…