UROP Opportunities


Human Motion and Facial Signatures





Given motion capture samples of Charlie Chaplin's walk, is it possible to synthesize other motions---say, ascending or descending stairs---in his distinctive style? More generally, in analogy with handwritten signatures, do people have characteristic motion signatures that individualize their movements? If so, can these signatures be extracted from example motions? Furthermore, can extracted signatures be used to recognize, say, a particular individual's walk subsequent to observing examples of other movements produced by this individual?


We are developing algorithms that extract motion signatures and are using them in the animation of graphical characters. The mathematical basis of our algorithm is a statistical technique formulated in terms of tensor algebra. For example, given a corpus of walking, stair ascending, and stair descending motion data collected over a group of subjects, plus a sample walking motion for a new subject, our algorithms can synthesize never before seen ascending and descending motions in the distinctive style of this new individual.


We are also applying our tensor algebraic approach to ensembles of human facial images in order to extract facial signatures that characterize the appearance of particular individuals. We will use these facial signatures to synthesize realistic and non-photorealistic images of people.


This project will introduce students to computer graphics, computer vision, and machine learning techniques.



Prerequisites: Linear algebra and/or signal processing










Contact Information:

M. Alex O. Vasilescu

Media Lab

Office: E15-321

E-mail: maov@media.mit.edu

Phone: x2-5634