Qiang Hu

Assistant Researcher

Shanghai Jiao Tong University

I am currently an Assistant Researcher at Cooperative Medianet Innovation Center, Shanghai Jiao Tong University. Before that, I was an Assistant Researcher working with Prof. Jingyi Yu at ShanghaiTech University from 2021 to 2023. I was a Postdoc Researcher at ShanghaiTech University from 2019 to 2021. I received a Ph.D. degree in information and communication engineering from Shanghai Jiao Tong University in 2019 and a B. Eng. degree from University of Electronic Science and Technology of China in 2013. My research interests focus on video compression, neural rendering, and free-viewpoint video streaming.

News

12/2023, One paper is accepted to AAAI 2024

10/2023, I join Shanghai Jiao Tong University as an Assistant Researcher

06/2023, One paper is accepted to ICCP 2023

03/2023, One paper is accepted to CVPR 2023

01/2023, My homepage is online.

Recent Publications

[AAAI'2024] Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification

Yuteng Ye, Hang Zhou, Junqing Yu, Qiang Hu, Wei Yang

arXiv preprint arXiv:2211.14742, 2022.
Arxiv Bibtex (Coming soon)

[ICCP'2023] NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Rendering

Ziyu Wang, Wei Yang, Junming Cao, Qiang Hu, Lan Xu, Junqing Yu, Jingyi Yu

IEEE International Conference on Computational Photography (ICCP), 2023.
Paper Bibtex (Coming soon)

[CVPR'2023] Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos

Liao Wang, Qiang Hu, Qihan He, Ziyu Wang, Jingyi Yu, Tinne Tuytelaars, Lan Xu, Minye Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
Paper Code & Project Video Bibtex

[Arxiv'2023] NEPHELE: A Neural Platform for Highly Realistic Cloud Radiance Rendering

Haimin Luo, Siyuan Zhang, Fuqiang Zhao, Haotian Jing, Penghao Wang, Zhenxiao Yu, Dongxue Yan, Junran Ding, Boyuan Zhang, Qiang Hu, Shu Yin, Lan Xu, Jingyi Yu

arXiv preprint arXiv:2303.04086., 2023.
Project (Coming soon) Video (Coming soon) Arxiv

[CVPR'2022] Learning Based Multi-Modality Image and Video Compression

Guo Lu, Tianxiong Zhong, Jing Geng, Qiang Hu, Dong Xu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Paper Bibtex

[Arxiv'2022] Preprocessing Enhanced Image Compression for Machine Vision

Guo Lu, Xingtong Ge, Tianxiong Zhong, Jing Geng, Qiang Hu

arXiv preprint arXiv:2206.05650, 2022.
Arxiv

[TIP'2020] Visual Tracking With Multiview Trajectory Prediction

Minye Wu, Haibin Ling, Ning Bi, Shenghua Gao, Qiang Hu, Hao Sheng, Jingyi Yu

IEEE Transactions on Image Processing (TIP), 2020.
Paper Code & Project Bibtex

[CVPR'2020] Multi-view neural human rendering

Minye Wu, Yuehao Wang, Qiang Hu, Jingyi Yu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
Paper Code & Project Video Bibtex

[MM'2020] Neural3D: Light-weight Neural Portrait Scanning via Context-aware Correspondence Learning

Xin Suo, Minye Wu, Yanshun Zhang, Yingliang Zhang, Lan Xu, Qiang Hu, Jingyi Yu

Proceedings of the 28th ACM International Conference on Multimedia(MM), 2020.
Paper Video Bibtex

[MM'2020] LGNN: A Context-aware Line Segment Detector

Quan Meng, Jiakai Zhang, Qiang Hu, Xuming He, Jingyi Yu

Proceedings of the 28th ACM International Conference on Multimedia(MM), 2020.
Paper Bibtex

[ICIP'2018] Enhancing HEVC Compressed Videos with a Partition-Masked Convolutional Neural Network

Xiaoyi He, Qiang Hu, Xiaoyun Zhang, Chongyang Zhang, Weiyao Lin, Xintong Han

IEEE International Conference on Image Processing (ICIP), 2018.
Paper Bibtex

[TMM'2016] Neyman-Pearson-Based Early Mode Decision for HEVC Encoding

Qiang Hu, Xiaoyun Zhang, Zhiru Shi, Zhiyong Gao

IEEE Transactions on Multimedia (TMM), 2016.
Paper Bibtex

More Publications

Projects

Real-Time Interactive Free-Viewpoint Video Streaming

We have developed an integrated solution that delivers live and on-demand FVV streaming services in real-time. Our solution provides an immersive and interactive viewing experience for FVV content, even on lightweight devices via mobile networks. Furthermore, our system is optimized to support UHD FVV applications on such devices, offering users an unparalleled viewing experience.

Online Experience Video

Distribute the Power of Radiance Rendering via the Cloud

We adopt a streaming-based solution where computationally intensive workloads are conducted on cloud servers, and their results are sent to the client as a video stream. It enables an immersive and interactive viewing experience for the neural radiance field on lightweight devices (e.g., cell phones, VR headsets).

Online Experience