Xiaobin Hu (胡晓彬)

I am a research scientist at Tencent working closely with Dr. Ying Tai and Dr. Chengjie Wang through ‘腾讯技术大咖’ program. I receive Shanghai Overseas Talents Award (Baiyulan Young Talent Program) in 2023, Before that, I obtained my Ph.D. degree at the School of Computer Science and Engineering, Technische Universität München, Germany, under the joint supervision of Prof. Bjoern Menze and Prof. Kuangyu Shi. During Phd studies, I also worked as a long-term intern in Chinese Academy of Sciences with Prof. Wenqi Ren, and a half-year internship with Prof. Dengping Fan and Hang Dai in IIAI and Mbzuai of United Arab Emirates.

As a first or corresponding author, I have published over 15 papers on top-tier conferences and journals, such as ICCV, ECCV, TPAMI, MICCAI, JBHI, EJNM etc. (Total Top/Q1 Journal in Chinese Academy of Sciences ranking: 6; Top conference: 4;)

• Image restoration inculding super-resolution and deblurring tasks
• Neural architecture search
• 3D face and related Tasks
• Image to image translation
• Uncertainty analysis and interpretation
• Segmentation, detection and classification
• Domain adaptation
• Medical image analysis

  Email  /    Google Scholar  /    Github  / 

Selected Publications

Existing face restoration algorithms only employ 2D priors without considering high dimensional information (3D). The 3D morphable facial priors are the main novelty of this work and are completely different from recently related 2D prior works

In this paper, we propose a novel face super resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures. Our work is the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose)

we propose a novel deblurring method, dubbed PyNAS, towards automatically designing hyper-parameters including the scales, patches, and standard cell operators. Our primary contribution is a real-time deblurring algorithm (around 58 fps) for 720p images while achieves state-of-the-art deblurring performance on the GoPro and Video Deblurring datasets.

Awards / Honors Challenge