Automatic learning of articulated skeletons from 3D marker trajectories

Abstract. We present a novel fully-automatic approach for estimating an articulated skeleton of a moving subject and its motion from body marker trajectories that have been measured with an optical motion capture system. Our method does not require a priori information about the shape and proportions of the tracked subject, can be applied to arbitrary motion sequences, and renders dedicated initialization poses unnecessary. To serve this purpose, our algorithm first identifies individual rigid bodies by means of a variant of spectral clustering. Thereafter, it determines joint positions at each time step of motion through numerical optimization, reconstructs the skeleton topology, and finally enforces fixed bone length constraints. Through experiments, we demonstrate the robustness and efficiency of our algorithm and show that it outperforms related methods from the literature in terms of accuracy and speed.

Sufficient descent directions in unconstrained optimization

Unconstrained optimization, Sufficient descent direction, PSB method, Global convergence, Superlinear convergence,