In computer animation, human motion capture from video is a widely used technique to acquire motion parameters. The acquisition process typically requires an intrusion into the scene in the form of optical markers which are used to estimate the parameters of motion as well as the kinematic structure of the performer.  Marker-free optical motion capture approaches exist, but due to their dependence on a specific type of a-priori model they can hardly be used to track other subjects, e.g. animals. To bridge the gap between the generality of marker-based methods and the applicability of marker-free methods we study a flexible nonintrusive approach that estimates both, a kinematic model and its parameters of motion from a sequence of voxel-volumes. The volume sequences are reconstructed from multi-view video data by means of a shape from-silhouette technique. The method [1] is well-suited for but not limited to motion capture of human subjects.
 

For realistic animation of an artificial character a body model that represents the character’s kinematic structure is required. Hierarchical skeleton models are widely used which represent bodies as chains of bones with interconnecting joints. In video motion capture, animation parameters are derived from the performance of a subject in the real world. For this acquisition procedure too, a kinematic body model is required. Typically, the generation of such a model for tracking and animation is, at best, a semi-automatic process. We study a novel approach that estimates a hierarchical skeleton model of an arbitrary moving subject from sequences of voxel data that were reconstructed from multi-view video footage. Our method [2] does not require a-priori information about the body structure.