[4eyes] Talk: Estimating Human Body Shape from Images

[Matthew Turk]

Michael Black, an excellent computer vision researcher and CS professor at
Brown University, will be visiting on Monday, February 22. He'll give a talk
in CS at 3:30pm - see below.

Please let me know ASAP if you'd like to meet with Michael sometime on
Monday.

	Matthew

-----Original Message-----
From: Michael Black [mailto:black at cs.brown.edu] 
Sent: Friday, February 12, 2010 4:16 PM
To: Matthew Turk
Cc: Michael Black
Subject: Abstract and bio


Estimating Human Body Shape from Images

Michael J. Black

Body shape is central to understanding gender, identity, age, etc.,  
yet it's estimation from images and video is not well studied. Unlike  
rigid 3D scenes, humans are highly non-rigid and articulated. They  
also wear clothing that moves and obscures their form.  This talk will  
explore our recent work on body shape estimation and the pieces needed  
to solve this problem.  First we show how a parametric 3D human body  
model (SCAPE) can be used to estimate body shape from image  
measurements in a calibrated and controlled environment.  This model  
has the important property of factoring body shape variation due to  
identity from body shape variation due to pose.  Second, we exploit  
this factorization to recover the shape of a person appearing in  
different poses.  By assuming body shape is constant across pose, we  
are able to infer body shape under clothing.  Finally we show how  
shading cues can be used to estimate body shape from a single image or  
painting.   Several applications of body-shape-from-video will be  
discussed.


Related papers:
Estimating human shape and pose from a single image (ICCV'09)

http://www.cs.brown.edu/~black/Papers/guanICCV09.pdf

The naked truth: Estimating human shape under clothing (ECCV'08)

http://www.cs.brown.edu/~black/Papers/balanECCV08small.pdf

Recovering human pose and shape in strong lighting (ICCV'07)

http://www.cs.brown.edu/~black/Papers/illumSCAPE07.pdf

Detailed human shape and pose from images (CVPR'07)

http://www.cs.brown.edu/~black/Papers/balan07imscape.pdf


Bio:
Michael Black received his B.Sc. from the University of British  
Columbia (1985), his M.S. from Stanford (1989), and his Ph.D. in  
computer science from Yale University in 1992. He has been a visiting  
researcher at the NASA Ames Research Center and an Assistant Professor  
in the Dept. of Computer Science at the University of Toronto. In 1993  
Prof. Black joined the Xerox Palo Alto Research Center where he  
managed the Image Understanding area and later founded the Digital  
Video Analysis group. In 2000, Prof. Black joined the faculty of Brown  
University where he is a Professor of Computer Science. At CVPR'91 he  
received the IEEE Computer Society Outstanding Paper Award for his  
work with P. Anandan on robust optical flow estimation. His work also  
received Honorable Mention for the Marr Prize in 1999 (with David  
Fleet) and 2005 (with Stefan Roth). Prof. Black's research interests  
in machine vision include optical flow estimation, human motion  
analysis and probabilistic models of the visual world. In  
computational neuroscience his work focuses on probabilistic models of  
the neural code, the neural control of movement and the development of  
neural interface systems that directly connect brains and machines to  
restore lost function to people with central motor system injury.