Inverse kinematics based on high-order moments of feature points and their Jacobian matrices

In this paper, we propose the inverse kinematics method based on high-order moment features and their Jacobian matrices, which can use an arbitrary information source about the shape of the targeted kinematic chain as reference input. The method is especially useful to generate the motion of humanoid robots and human figures, and we can generate the whole body pose from a set of 3D markers or pixels of 2D images, without labeling each feature point. The moment features are computed from various types of quantities, for example, geometric points, mass density, pixel images, and the probability labeled as a specific link, the fact of which shows generality and versatility of the method. Some results of motion of a human figure from the label-less 3D markers and the 2D images are illustrated.