Efficient Hand-Over Motion Reconstruction

Abstract

In this paper, a hand-over motion reconstruction methodology is presented. First to be examined in the proposed approach is the way in which the system computes the optimal markerset for a given dataset of hand motion sequences. In a second step before the motion reconstruction process and given the reduced number of markers, the system estimates the remaining markers by computing a simple distance metric. Having the complete number of markers, including both the input and the computed markers, the system reconstructs the motion of the character’s fingers. The reconstruction process is formulated in a maximum a posteriori framework, which is responsible for approximating a valid pose of the character’s hand, in which the mixture of factor analysis (MFA) clustering techniques was used for the prior learning process. The results show that high quality motions of the character’s hand can be reconstructed with the methodology presented.