Predicting Human Body Shape Under Clothing

Speaker: Prof. Michael J. Black, Brown University
Host: Prof. Gerard MedioniAbstract:
We propose a method to estimate the detailed 3D shape of a person from images of that person wearing clothing. The approach exploits a model of human body shapes that is learned from a database of over 2000 range scans. We show that the parameters of this shape model can be recovered independently of body pose. We further propose a generalization of the visual hull to account for the fact that observed silhouettes of clothed people do not provide a tight bound on the true 3D shape. With clothed subjects, different poses provide different constraints on the possible underlying 3D body shape. We consequently combine constraints across pose to more accurately estimate 3D body shape in the presence of occluding clothing. Finally we use the recovered 3D shape to estimate the gender of subjects and then employ gender-specific body models to refine our shape estimates. Results on a novel database of thousands of images of clothed and ``naked'' subjects, as well as sequences from the HumanEva dataset, suggest the method may be accurate enough for biometric shape analysis in video.This is joint work with Alexandru Balan. Project page: http://www.cs.brown.edu/~alb/scapeClothing/Related ECCV paper: http://www.cs.brown.edu/~black/Papers/balanECCV08.pdfBiography:
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.

Location: Seaver Science Library (SSL) - 150

Audiences: Everyone Is Invited

Contact: CS Colloquia