We represent the state of the world in a low-dimensional subspace, called “pose”, which is a succinct interpretable representation of important information in the state. The state can then be estimated and predicted from this low-dimensional representation. The pose can also be used as a semi-supervised generative model to render and expand the labelled examples in the state space for the purpose of data augmentation for deep learning algorithms.

At Augmented Cognition Lab (ACLab), currently we work on three active projects, in which they explore different aspects of pose estimations (the cited papers are available at ACLab webpage alongside their datasets/codes):

(1) Articulated/Deformable Body Pose Estimation:

(2) Affective Pose Estimation:

(3) Environment Pose and Scene Understanding:

Funding: National Science Foundation (NSF), MathWorks, Amazon Web Services (AWS), INVIDIA.