Seminar Announcement
Déjà Vécu: Learning from Legacy MoCap Data for
Robust Human Action Recognition
Dr. Ajmal Mian of the University of Western Australia
Monday, December 11, 2017 · 4:00PM · HEC 101
Abstract
Annotating human action videos is tedious and expensive. We bypass this step and capitalize on legacy
motion capture (MoCap) data to synthesize video frames. The MoCap data is fitted with synthetic 3D
humans with varying sizes, gender and clothing, placed in random backgrounds and rendered from 180
camera viewpoints under random lighting conditions. In essence, actions performed by real humans in
the past are lived through in different bodies, clothes and locations giving us a large corpus of RGB and
depth images where the exact human pose is known. Since, synthetic and real images come from
different distributions, we perform unsupervised generative adversarial training to minimize the
distribution gap. From the refined synthetic images, we learn Human Pose Models (CNNs) that map an
input image to one of representative human poses learned by clustering the MoCap data. The trained
CNNs generalize well to extract invariant features from real images. Fourier Temporal Pyramid over the
CNN features is used to model videos and classification is performed with SVM. Experiments on three
cross-view human action datasets show that our algorithm outperforms existing methods by significant
margins for RGB only and RGB-D action recognition. Interestingly, our RGB only model outperforms
existing RGB-D methods on the most challenging NTU dataset. Finally, I will show how our data
synthesis paradigm generalizes to other applications such as human pose estimation, 3D face
recognition and depth estimation from Light Field Images.
Biography
Ajmal Mian is an Associate Professor of Computer Science at The University of Western Australia. He
has received several awards including the West Australian Early Career Scientist of the Year Award, the
Vice-chancellors Mid-career Research Award, the Aspire Professional Development Award, the
Outstanding Young Investigator Award, EH Thompson Award for best paper in photogrammetry and
the IAPR Best Paper Award. He has received two prestigious fellowships and seven major grants from
the Australian Research Council and the National Health and Medical Research Council with a total
funding of over $3.0 Million. He has published 150 scientific papers and edited special issues in three
journals. His major research interests are in computer vision, machine learning, 3D face analysis, human
action recognition and hyperspectral image analysis. He has also published multidisciplinary research
in marine science, neurodevelopmental disorders, agriculture, medicine and sleep science.
