CMU talk: Human Pose and Motion, Challenges and Physics-based Models

The VASC/DRP morning seminar series.

Title: Human Pose and Motion, Challenges and Physics-based Models

Leonid Sigal, University of Toronto

Date: Tuesday 3/31

Abstract:
Recovery and analysis of human pose and motion from video is the key enabling technology for a broad spectrum of applications, in and outside of computer science; including applications in HCI, biometrics, biomechanics and computer graphics. Despite years of research, the general problem of tracking a person in an unconstrained environment, particularly from monocular observations, remains challenging. In this talk I will describe the basic building blocks and challenges of the articulated human pose estimation and tracking, as well as my contributions to the various aspects of this problem and the field in recent years. I will particularly focus on the new and unique class of models that incorporate physic-based predictions and simulation into the inference process. Physics plays an important and intricate role in characterizing, describing and predicting human motion. The key benefit of using physics-based models or priors for tracking is the improved realism in the recovered motions, as well as enhanced ability to deal with weak image observations and diverse environmental interactions. Newtonian physics, in these models, approximates the rigid-body dynamics of the body in the environment through the application and integration of forces. Since the motion of the body is intimately tied with the environment, the use of such models also allows one to start reasoning about the geometry and physical properties of the environment as a whole (e.g. orientation and compliance of ground). This work is part of joint projects with colleagues at Brown University and University of Toronto.

Biography:
Leonid Sigal is a postdoctoral fellow in the Department of Computer Science at University of Toronto. He received his Ph.D. in computer science from Brown University (2007); his M.S. from Brown University (2003); his M.A. from Boston University (1999); and his B.Sc. degrees in Computer Science and Mathematics from Boston University (1999). From 1999 to 2001, he worked as a senior vision engineer at Cognex Corporation, where he developed industrial vision applications for pattern analysis and verification. In 2002, he spent a semester as a research intern at Siemens Corporate Research (SCR) working on autonomous obstacle detection and avoidance for vehicle navigation. During the summers of 2005 and 2006, he worked as a research intern at Intel Applications Research Lab (ARL) on human pose estimation and tracking. His work received the Best Paper Award at the Articulate Motion and Deformable Objects Conference in 2006 (with Prof. Michael J. Black). Dr. Sigal's research interests mainly lie in the areas of computer vision and machine learning, but also borderline fields of computer graphics, psychology and humanoid robotics. He is particularly interested in statistical models for problems of visual inference, including human motion recovery and analysis, graphical models, probabilistic and hierarchical inference.