Search Results for author: Xiangyu Xu

Found 20 papers, 11 papers with code

Learning Factorized Weight Matrix for Joint Image Filtering

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.

Video Frame Interpolation Transformer

no code implementations27 Nov 2021 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.

Video Frame Interpolation

Investigating Tradeoffs in Real-World Video Super-Resolution

1 code implementation24 Nov 2021 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.

Video Super-Resolution

STransGAN: An Empirical Study on Transformer in GANs

no code implementations25 Oct 2021 Rui Xu, Xiangyu Xu, Kai Chen, Bolei Zhou, Chen Change Loy

In this paper, we conduct a comprehensive empirical study to investigate the intrinsic properties of Transformer in GAN for high-fidelity image synthesis.

Image Generation

GTT-Net: Learned Generalized Trajectory Triangulation

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.

Event Segmentation Motion Capture

3D Human Texture Estimation from a Single Image with Transformers

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.

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

1 code implementation27 Apr 2021 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.

Video Enhancement Video Restoration +1

3D Human Pose, Shape and Texture from Low-Resolution Images and Videos

2 code implementations11 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.

Contrastive Learning Super-Resolution

Exploiting Raw Images for Real-Scene Super-Resolution

1 code implementation2 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.

Image Restoration Image Super-Resolution

Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising

3 code implementations26 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.

Image Denoising Video Denoising

GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution

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).

GAN inversion Image Super-Resolution

3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning

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.

3D Human Pose Estimation 3D Shape Reconstruction +4

Quadratic video interpolation

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.

Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction

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.

3D Reconstruction Event Segmentation +1

Towards Real Scene Super-Resolution with Raw Images

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.

Image Super-Resolution

Learning Deformable Kernels for Image and Video Denoising

2 code implementations15 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.

Image Denoising Video Denoising

Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement

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.

Monocular Depth Estimation Stereo Matching +1

Rendering Portraitures from Monocular Camera and Beyond

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.

Image Matting Portrait Segmentation

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