(with Hedvig Sidenbladh and Fernando De la Torre)
This paper describes a framework for constructing a linear subspace model of image appearance for complex articulated 3D figures such as humans and other animals. A commercial motion capture system provides 3D data that is aligned with images of subjects performing various activities. Portions of a limb's image appearance are seen from multiple views and for multiple subjects. From these partial views, weighted principal component analysis is used to construct a linear subspace representation of the ``unwrapped'' image appearance of each limb. The linear subspaces provide a generative model of the object appearance that is exploited in a Bayesian particle filtering tracking system. Results of tracking single limbs and walking humans are presented.
Sidenbladh, H., De la Torre, F., Black, M. J., A framework for modeling the appearance of 3D articulated figures, Int. Conf. on Automatic Face and Gesture Recognition, Grenoble, France, pp. 368-375, March 2000. (postscript)
Sidenbladh, H., Black, M. J., and Fleet, D.J., Stochastic tracking of 3D human figures using 2D image motion, to appear: European Conference on Computer Vision, Dublin, Ireland, June 2000. (postscript), (abstract)