Search Results for author: Xintao Wang

Found 28 papers, 20 papers with code

Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis

no code implementations6 Dec 2022 YuChao Gu, Xintao Wang, Yixiao Ge, Ying Shan, XiaoHu Qie, Mike Zheng Shou

Vector-Quantized (VQ-based) generative models usually consist of two basic components, i. e., VQ tokenizers and generative transformers.

GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond

1 code implementation29 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.

Colorization Image Colorization +2

FaceFormer: Scale-aware Blind Face Restoration with Transformers

no code implementations20 Jul 2022 Aijin Li, Gen Li, Lei Sun, Xintao Wang

Blind face restoration usually encounters with diverse scale face inputs, especially in the real world.

Blind Face Restoration

Language Models as Knowledge Embeddings

1 code implementation25 Jun 2022 Xintao Wang, Qianyu He, Jiaqing Liang, Yanghua Xiao

In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods.

Contrastive Learning Link Prediction +1

AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos

1 code implementation14 Jun 2022 Yanze Wu, Xintao Wang, Gen Li, Ying Shan

This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals three key improvements for practical animation VSR.

Video Super-Resolution

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration

VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder

1 code implementation13 May 2022 YuChao Gu, Xintao Wang, Liangbin Xie, Chao Dong, Gen Li, Ying Shan, Ming-Ming Cheng

Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods.

Blind Face Restoration Quantization

RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization

no code implementations11 May 2022 Xintao Wang, Chao Dong, Ying Shan

Extensive experiments demonstrate that our simple RepSR is capable of achieving superior performance to previous SR re-parameterization methods among different model sizes.

Super-Resolution

MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution

1 code implementation10 May 2022 Chong Mou, Yanze Wu, Xintao Wang, Chao Dong, Jian Zhang, Ying Shan

Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner.

Image Restoration Metric Learning +1

Accelerating the Training of Video Super-Resolution Models

no code implementations10 May 2022 Lijian Lin, Xintao Wang, Zhongang Qi, Ying Shan

In this work, we show that it is possible to gradually train video models from small to large spatial/temporal sizes, i. e., in an easy-to-hard manner.

Video Super-Resolution

Activating More Pixels in Image Super-Resolution Transformer

1 code implementation9 May 2022 Xiangyu Chen, Xintao Wang, Jiantao Zhou, Chao Dong

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution.

Image Super-Resolution

Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution

1 code implementation NeurIPS 2021 Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, Ying Shan

Unlike previous integral gradient methods, our FAIG aims at finding the most discriminative filters instead of input pixels/features for degradation removal in blind SR networks.

Blind Super-Resolution Super-Resolution

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

6 code implementations22 Jul 2021 Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan

Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.

Blind Super-Resolution Video Super-Resolution

Robust Reference-based Super-Resolution via C2-Matching

1 code implementation CVPR 2021 Yuming Jiang, Kelvin C. K. Chan, Xintao Wang, Chen Change Loy, Ziwei Liu

However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e. g. scale and rotation) and the resolution gap (e. g. HR and LR).

Reference-based Super-Resolution

Towards Real-World Blind Face Restoration with Generative Facial Prior

1 code implementation CVPR 2021 Xintao Wang, Yu Li, Honglun Zhang, Ying Shan

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details.

Blind Face Restoration Video Super-Resolution

Positional Encoding as Spatial Inductive Bias in GANs

no code implementations CVPR 2021 Rui Xu, Xintao Wang, Kai Chen, Bolei Zhou, Chen Change Loy

In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators.

Image Manipulation Inductive Bias +1

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).

Image Super-Resolution

Understanding Deformable Alignment in Video Super-Resolution

no code implementations15 Sep 2020 Kelvin C. K. Chan, Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy

Aside from the contributions to deformable alignment, our formulation inspires a more flexible approach to introduce offset diversity to flow-based alignment, improving its performance.

Optical Flow Estimation Video Super-Resolution

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

11 code implementations7 May 2019 Xintao Wang, Kelvin C. K. Chan, Ke Yu, Chao Dong, Chen Change Loy

In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.

Deblurring Video Enhancement +2

Path-Restore: Learning Network Path Selection for Image Restoration

1 code implementation23 Apr 2019 Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, Chen Change Loy

To leverage this, we propose Path-Restore, a multi-path CNN with a pathfinder that can dynamically select an appropriate route for each image region.

Denoising Image Restoration +1

Deep Network Interpolation for Continuous Imagery Effect Transition

2 code implementations CVPR 2019 Xintao Wang, Ke Yu, Chao Dong, Xiaoou Tang, Chen Change Loy

Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect.

Image Restoration Image-to-Image Translation +2

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

38 code implementations1 Sep 2018 Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang

To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).

Face Hallucination Image Super-Resolution +2

Cannot find the paper you are looking for? You can Submit a new open access paper.