47 citations · 153 across the 15 of their papers we have counts for
16 papers
VEnhancer: Generative Space-Time Enhancement for Video Generation
Jingwen He, Tianfan Xue, Dongyang Liu +6
We present VEnhancer, a generative space-time enhancement framework that improves the existing text-to-video results by adding more details in spatial domain and synthetic detailed…
Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
Peng Gao, Le Zhuo, Dongyang Liu +17
Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lac…
Towards Real-world Video Face Restoration: A New Benchmark
Ziyan Chen, Jingwen He, Xinqi Lin +2
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 c…
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Fanghua Yu, Jinjin Gu, Zheyuan Li +6
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-mod…
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
Xiangyu Chen, Zheyuan Li, Zhengwen Zhang +6
Modern displays can render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most resources are still in standard dynamic range (SDR). Therefore, tra…
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
Xinqi Lin, Jingwen He, Ziyan Chen +6
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework. DiffBIR decouples blind image restoration probl…
Structured Sparsity Learning for Efficient Video Super-Resolution
Bin Xia, Jingwen He, Yulun Zhang +4
The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, (e.g., smartphones and drones). Existing VSR models contain…
Residual Local Feature Network for Efficient Super-Resolution
Fangyuan Kong, Mingxi Li, Songwei Liu +5
Deep learning based approaches has achieved great performance in single image super-resolution (SISR). However, recent advances in efficient super-resolution focus on reducing the…
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
Jingwen He, Wu Shi, Kai Chen +2
Face image super resolution (face hallucination) usually relies on facial priors to restore realistic details and preserve identity information. Recent advances can achieve impress…
Toward Interactive Modulation for Photo-Realistic Image Restoration
Haoming Cai, Jingwen He, Qiao Yu +1
Modulating image restoration level aims to generate a restored image by altering a factor that represents the restoration strength. Previous works mainly focused on optimizing the…
Very Lightweight Photo Retouching Network with Conditional Sequential Modulation
Yihao Liu, Jingwen He, Xiangyu Chen +4
Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects, especially for poor contrast, over/under exposure, and inharmonious…
Efficient Image Super-Resolution Using Pixel Attention
Hengyuan Zhao, Xiangtao Kong, Jingwen He +2
This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective ne…
Conditional Sequential Modulation for Efficient Global Image Retouching
Jingwen He, Yihao Liu, Yu Qiao +1
Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation.…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration
Jingwen He, Chao Dong, Yu Qiao
Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modul…
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
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…