Ming-Yu Liu is a senior research scientist at Nvidia Research. Prior to joining NVIDIA, he was a principal research scientist at Mitsubishi Electric Research Labs (MERL). He received his Ph.D. from the Department of Electrical and Computer Engineering at the University of Maryland College Park in 2012 and B.S. degree from National Chiao Tung University in Taiwan in 2003. His research interests are on computer vision and deep learning. His early research work on object pose estimation contributed to development of the first commercial vision-based robotic bin-picking system for robotic assembly tasks, which was awarded the 100 most innovative technology products of the year by the R&D magazine in 2014. He is a recipient of a paper award from the robotics science and system (RSS) 2015 conference for his street scene understanding work. Recently, his research focus shifted to deep generative models for image understanding and generation. His goal is to enable machines superhuman-like imagination capabilities. A copy of my resume can be found in link.


  • 04-24-2017: One paper accepted by IJCAI

  • 04-10-2017: A PyTorch implementation of the coupled generative adversarial networks is released in GitHub via [CoGAN_PyTorch].




Patents granted

  • US 9,633,274: Method and system for denoising images using deep Gaussian conditional random field network

  • US 9,558,268: Method for semantically labeling an image of a scene using recursive context propagation

  • US 8,428,363: Method for segmenting images using superpixels and entropy rate clustering

  • US 8,983,177: Method for increasing resolutions of depth images

  • US 8,908,913: Voting-based pose estimation for 3D sensors

  • US 9,195,904: Method for detecting objects in stereo images

  • US 9,280,827: Method for determining object poses using Weighted Features

  • My MERL patents


  • PhD, Electrical&Computer Engineering, University of Maryland College Park, Advisor: Rama Chellappa, 2006-2012


  • CVPR2017: Theory and Applications of Generative Adversarial Networks

  • ACCV2016: Deep Learning for Vision Guided Language Generation and Image Generation [Site]