Search Results for author: Jingwen He

Found 16 papers, 11 papers with code

Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers

1 code implementation9 May 2024 Peng Gao, Le Zhuo, Dongyang Liu, Ruoyi Du, Xu Luo, Longtian Qiu, Yuhang Zhang, Chen Lin, Rongjie Huang, Shijie Geng, Renrui Zhang, Junlin Xi, Wenqi Shao, Zhengkai Jiang, Tianshuo Yang, Weicai Ye, He Tong, Jingwen He, Yu Qiao, Hongsheng Li

Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details.

Towards Real-world Video Face Restoration: A New Benchmark

no code implementations30 Apr 2024 Ziyan Chen, Jingwen He, Xinqi Lin, Yu Qiao, Chao Dong

Blind face restoration (BFR) on images has significantly progressed over the last several years, while real-world video face restoration (VFR), which is more challenging for more complex face motions such as moving gaze directions and facial orientations involved, remains unsolved.

Blind Face Restoration Image Quality Assessment +1

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

no code implementations24 Jan 2024 Fanghua Yu, Jinjin Gu, Zheyuan Li, JinFan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, Chao Dong

We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up.

Descriptive Image Restoration

DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

1 code implementation29 Aug 2023 Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Bo Dai, Fanghua Yu, Wanli Ouyang, Yu Qiao, Chao Dong

We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework.

Blind Face Restoration Image Denoising +2

DegAE: A New Pretraining Paradigm for Low-Level Vision

1 code implementation CVPR 2023 Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, Chao Dong

However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging.


Structured Sparsity Learning for Efficient Video Super-Resolution

1 code implementation CVPR 2023 Bin Xia, Jingwen He, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Luc van Gool

In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks.

Video Super-Resolution

Residual Local Feature Network for Efficient Super-Resolution

2 code implementations16 May 2022 Fangyuan Kong, Mingxi Li, Songwei Liu, Ding Liu, Jingwen He, Yang Bai, Fangmin Chen, Lean Fu

Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance.

Image Super-Resolution SSIM

GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors

no code implementations CVPR 2022 Jingwen He, Wu Shi, Kai Chen, Lean Fu, Chao Dong

The style modulation aims to generate realistic face details and the feature modulation dynamically fuses the multi-level encoded features and the generated ones conditioned on the upscaling factor.

Face Hallucination Hallucination +1

Efficient Image Super-Resolution Using Pixel Attention

1 code implementation2 Oct 2020 Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong

Pixel attention (PA) is similar as channel attention and spatial attention in formulation.

Image Super-Resolution

Conditional Sequential Modulation for Efficient Global Image Retouching

1 code implementation ECCV 2020 Jingwen He, Yihao Liu, Yu Qiao, Chao Dong

The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector.

Image Retouching Photo Retouching

Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration

1 code implementation ECCV 2020 Jingwen He, Chao Dong, Yu Qiao

To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels.

Image Restoration

Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

1 code implementation CVPR 2019 Jingwen He, Chao Dong, Yu Qiao

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods.

Image Denoising Image Restoration +1

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