Leonid Sigal

Disney Research Pittsburgh / Department of Computer Science, Carnegie Mellon University

 
 
 

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Disney Research Pittsburgh
4615 Forbes Ave
Pittsburgh, PA 15213

Phone: (412) 802-6154
Email: lsigal at disneyresearch.com
 
 
 

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About Me ...

Leonid Sigal's Photo

I am a Research Scientist at Disney Research Pittsburgh, in conjunction with Carnegie Mellon University. My current research focuses on articulated motion capture, manifold learning, character animation and a number of other directions on the fringe of computer vision, machine learning, and computer graphics. I work in colaboration with Prof. Jessica Hodgins and a number of other excellent researchers. Before coming to Disney Research and CMU, I spent 2 years working as a Postdoctoral Fellow in the Department of Computer Science at University of Toronto (UofT). At UofT I worked with Prof. David Fleet and collaborated with a number of his, as well as Prof. Geoff Hinton's students. During my time at UofT I also twice taught an undergraduate Computer Graphics course at University of Toronto, Scarborough.

My doctorate dissertation, Continuous-state Graphical Models for Object Localization, Pose Estimation and Tracking, was written under the supervision of my advisor Prof. Michael J. Black at Brown University. I received my Master's Degree in Computer Science and dual undergraduate degrees in Computer Science and Applied Mathematics from Boston University, where I worked with Prof. Stan Sclaroff.

I also try to maintain an active professional service within the community. As part of that service I regularly review papers for major computer vision and machine learning conferences, and have orginized a number of workshops and, more recently, a tutorial. My full bio and CV can be found here.

My research interests mainly lie in the areas of computer vision, machine learning, and computer graphics. I am particularly interested in statistical models for problems of visual inference. I am probably most known for my work on articulated pose estimation and tracking. More recently I have also conducted research on the articulated shape estimation. My most recent work focuses on the physics-based dynamical simulation priors for articulated human motion modeling and tracking.

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