  We analyze human poses and motion by introducing three sequences of easily
calculated surface descriptors that are invariant under reparametrizations and
Euclidean transformations. These descriptors are obtained by associating to
each finitely-triangulated surface two functions on the unit sphere: for each
unit vector u we compute the weighted area of the projection of the surface
onto the plane orthogonal to u and the length of its projection onto the line
spanned by u. The L2 norms and inner products of the projections of these
functions onto the space of spherical harmonics of order k provide us with
three sequences of Euclidean and reparametrization invariants of the surface.
The use of these invariants reduces the comparison of 3D+time surface
representations to the comparison of polygonal curves in R^n. The experimental
results on the FAUST and CVSSP3D artificial datasets are promising. Moreover, a
slight modification of our method yields good results on the noisy CVSSP3D real
dataset.
