  We address the problem of human motion tracking by registering a surface to
3-D data. We propose a method that iteratively computes two things: Maximum
likelihood estimates for both the kinematic and free-motion parameters of a
kinematic human-body representation, as well as probabilities that the data are
assigned either to a body part, or to an outlier cluster. We introduce a new
metric between observed points and normals on one side, and a parameterized
surface on the other side, the latter being defined as a blending over a set of
ellipsoids. We claim that this metric is well suited when one deals with either
visual-hull or visual-shape observations. We illustrate the method by tracking
human motions using sparse visual-shape data (3-D surface points and normals)
gathered from imperfect silhouettes.
