3D Shape Sequence of Human Comparison and Classification Using Current and Varifolds
"In this paper we address the task of the comparison and the classification of 3D shape sequences of human. The non-linear dynamics of the human motion and the changing of the surface parametrization over the time make this task very challenging. To tackle this issue, we propose to embed the 3D shape sequences in an infinite dimensional space, the space of varifolds, endowed with an inner product that comes from a given positive definite kernel. More specifically, our approach involves two steps: 1) the surfaces are represented as varifolds, this representation induces metrics equivariant to rigid motions and invariant to parametrization; 2) the sequences of 3D shapes are represented by Gram matrices derived from their infinite dimensional Hankel matrices, and we use Frobenius distance between two Symmetric Positive definite (SPD) matrices to compare two sequences. Extensive experiments show that our method is competitive with state-of-the-art in 3D sequence motion retrieval."