no code implementations • ICML 2020 • Xiangyu Xu, Yongrui Ma, Wenxiu Sun
In this work, we propose to learn the weight matrix for joint image filtering.
no code implementations • 8 Jan 2023 • Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, Shuicheng Yan
For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i. e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective.
no code implementations • 8 Dec 2022 • Xiangyu Xu, Li Guan, Enrique Dunn, Haoxiang Li, Gang Hua
In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization.
no code implementations • 25 Oct 2022 • Zhiqi Zhang, Nitin Bansal, Changjiang Cai, Pan Ji, Qingan Yan, Xiangyu Xu, Yi Xu
To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain.
1 code implementation • 29 Jul 2022 • Kelvin C. K. Chan, Xiangyu Xu, Xintao Wang, Jinwei Gu, Chen Change Loy
While most existing perceptual-oriented approaches attempt to generate realistic outputs through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN.
1 code implementation • 18 Apr 2022 • Kang Liao, Xiangyu Xu, Chunyu Lin, Wenqi Ren, Yunchao Wei, Yao Zhao
Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a consistent and seamless cylinder.
1 code implementation • 11 Apr 2022 • Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy
The exploitation of long-term information has been a long-standing problem in video restoration.
no code implementations • 8 Dec 2021 • Mingfei Chen, Jianfeng Zhang, Xiangyu Xu, Lijuan Liu, Yujun Cai, Jiashi Feng, Shuicheng Yan
Meanwhile, for achieving higher rendering efficiency, we introduce a progressive rendering pipeline through geometry guidance, which leverages the geometric feature volume and the predicted density values to progressively reduce the number of sampling points and speed up the rendering process.
1 code implementation • CVPR 2022 • Zhihao Shi, Xiangyu Xu, Xiaohong Liu, Jun Chen, Ming-Hsuan Yang
Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field.
1 code implementation • CVPR 2022 • Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy
The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training.
no code implementations • 25 Oct 2021 • Rui Xu, Xiangyu Xu, Kai Chen, Bolei Zhou, Chen Change Loy
Transformer becomes prevalent in computer vision, especially for high-level vision tasks.
no code implementations • ICCV 2021 • Xiangyu Xu, Enrique Dunn
We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry.
1 code implementation • ICCV 2021 • Xiangyu Xu, Chen Change Loy
We propose a Transformer-based framework for 3D human texture estimation from a single image.
3 code implementations • CVPR 2022 • Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy
We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively.
Ranked #1 on Video Enhancement on MFQE v2
1 code implementation • 21 Apr 2021 • Ren Yang, Radu Timofte, Jing Liu, Yi Xu, Xinjian Zhang, Minyi Zhao, Shuigeng Zhou, Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy, Xin Li, Fanglong Liu, He Zheng, Lielin Jiang, Qi Zhang, Dongliang He, Fu Li, Qingqing Dang, Yibin Huang, Matteo Maggioni, Zhongqian Fu, Shuai Xiao, Cheng Li, Thomas Tanay, Fenglong Song, Wentao Chao, Qiang Guo, Yan Liu, Jiang Li, Xiaochao Qu, Dewang Hou, Jiayu Yang, Lyn Jiang, Di You, Zhenyu Zhang, Chong Mou, Iaroslav Koshelev, Pavel Ostyakov, Andrey Somov, Jia Hao, Xueyi Zou, Shijie Zhao, Xiaopeng Sun, Yiting Liao, Yuanzhi Zhang, Qing Wang, Gen Zhan, Mengxi Guo, Junlin Li, Ming Lu, Zhan Ma, Pablo Navarrete Michelini, Hai Wang, Yiyun Chen, Jingyu Guo, Liliang Zhang, Wenming Yang, Sijung Kim, Syehoon Oh, Yucong Wang, Minjie Cai, Wei Hao, Kangdi Shi, Liangyan Li, Jun Chen, Wei Gao, Wang Liu, XiaoYu Zhang, Linjie Zhou, Sixin Lin, Ru Wang
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
2 code implementations • 11 Mar 2021 • Xiangyu Xu, Hao Chen, Francesc Moreno-Noguer, Laszlo A. Jeni, Fernando de la Torre
Two common approaches to deal with low-resolution images are applying super-resolution techniques to the input, which may result in unpleasant artifacts, or simply training one model for each resolution, which is impractical in many realistic applications.
1 code implementation • 2 Feb 2021 • Xiangyu Xu, Yongrui Ma, Wenxiu Sun, Ming-Hsuan Yang
In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images.
3 code implementations • 26 Jan 2021 • Xiangyu Xu, Muchen Li, Wenxiu Sun, Ming-Hsuan Yang
We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.
no code implementations • CVPR 2021 • Kelvin C. K. Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, Chen Change Loy
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-resolution (SR).
2 code implementations • ECCV 2020 • Xiangyu Xu, Hao Chen, Francesc Moreno-Noguer, Laszlo A. Jeni, Fernando de la Torre
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, from activity recognition to creating virtual avatars.
Ranked #17 on 3D Human Pose Estimation on MPI-INF-3DHP (PA-MPJPE metric)
1 code implementation • NeurIPS 2019 • Xiangyu Xu, Li Si-Yao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang
Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors.
no code implementations • ICCV 2019 • Xiangyu Xu, Enrique Dunn
We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution.
1 code implementation • CVPR 2019 • Xiangyu Xu, Yongrui Ma, Wenxiu Sun
Most existing super-resolution methods do not perform well in real scenarios due to lack of realistic training data and information loss of the model input.
2 code implementations • 15 Apr 2019 • Xiangyu Xu, Muchen Li, Wenxiu Sun
Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input.
no code implementations • ECCV 2018 • Yukang Gan, Xiangyu Xu, Wenxiu Sun, Liang Lin
While significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs) extracting absolute features, such as edges and textures, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent methods.
no code implementations • ECCV 2018 • Xiangyu Xu, Deqing Sun, Sifei Liu, Wenqi Ren, Yu-Jin Zhang, Ming-Hsuan Yang, Jian Sun
Specifically, we first exploit Convolutional Neural Networks to estimate the relative depth and portrait segmentation maps from a single input image.
no code implementations • ICCV 2017 • Xiangyu Xu, Deqing Sun, Jinshan Pan, Yu-Jin Zhang, Hanspeter Pfister, Ming-Hsuan Yang
We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input.