Guanzhi Wang

I am a Master's student at the Stanford Vision and Learning Lab, where I have been fortunate working with Prof. Fei-Fei Li, Prof. Yuke Zhu and Dr. Jim Fan.

I obtained my B.S. degree from the Hong Kong University of Science and Technology, where I have been lucky to work with Prof. Chi-Keung Tang and Prof. Yu-Wing Tai. Previously, I was a research intern at Tencent YouTu.

My research interests lie in the area of computer vision and robotics. I am passionate about building intelligent agents to acquire rich and robust visual representations proactively and solve real-world robotic tasks efficiently.

Email  /  Google Scholar  /  GitHub  /  LinkedIn

profile photo
News

Publications
(* indicates equal contribution)
iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes
Bokui Shen*, Fei Xia*, Chengshu Li*, Roberto Martín-Martín*, Linxi Fan, Guanzhi Wang, Shyamal Buch, Claudia D’Arpino, Sanjana Srivastava, Lyne P. Tchapmi, Micael E. Tchapmi, Kent Vainio, Li Fei-Fei, Silvio Savarese
International Conference on Robotics and Automation (ICRA), 2021 (under review)
[paper]   [project page]   [code]  

We presented iGibson, a novel simulation environment for developing interactive robotic agents in large-scale realistic scenes.

RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition
Linxi Fan*, Shyamal Buch*, Guanzhi Wang, Ryan Cao, Yuke Zhu, Juan Carlos Niebles, Li Fei-Fei
European Conference on Computer Vision (ECCV), 2020
[paper]   [project page]   [video]   [supplementary]   [code]  

We proposed RubiksNet, a new efficient architecture for video action recognition based on a proposed learnable 3D spatiotemporal shift operation (RubiksShift).


LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
Qiao Gu*, Guanzhi Wang*, Mang Tik Chiu, Yu-Wing Tai, Chi-Keung Tang
International Conference on Computer Vision (ICCV), 2019
[paper]   [project page]   [code]   [dataset]

We proposed a local adversarial disentangling network for facial makeup and de-makeup, using multiple and overlapping local discriminators in a content-style disentangling network.


Technical Reports
Drowsiness Tracking from Video Recordings Obtained from Consumer-grade Electronic Devices
Guanzhi Wang, Thomas Heldt
Technical Report, 2018
[paper]

We proposed a facial landmark based method to extract blink duration and to quantify drowsiness scale from video recordings obtained from consumer-grade electronic devices.

Velocity Vector Preserving Trajectory Simplification
Guanzhi Wang*, Zhenmei Shi*, Cheng Long, Ya Gao, Raymond Chi-Wing Wong
Technical Report, 2018
[paper]   [code]

We designed a novel algorithm to simplify a trajectory such that the number of points after simplification is minimized under the constraint that the velocity-based error does not exceed a given tolerance.


Teaching
cs229

CS129: Applied Machine Learning (Fall 2020)

CS229: Machine Learning (Spring 2020)

Teaching Assistant (TA)


Academic Services
eccv logo Conference Reviewer, CVPR 2021
eccv logo Conference Reviewer, ECCV 2020

Awards
  • Stanford Human-Centered AI AWS Cloud Credits Award (2020)
  • HKUST Academic Achievement Medal (2019)
  • Talent Development Scholarship (2019)
  • Reaching Out Award (2018)
  • High Fashion Charitable Foundation Exchange Scholarship (2018)
  • Overseas Learning Experience Scholarship (2018)
  • Dean’s List (2015-2019)
  • University Recruitment Scholarship (2015-2019)



This guy makes a nice webpage.