Event Location: NSH 3305
Bio: 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.

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.