Self-calibrating optical motion tracking for articulated bodies

Building intuitive user-interfaces for virtual reality applications is a difficult task, as one of the main purposes is to provide a "natural", yet efficient input device to interact with the virtual environment. One particularly interesting approach is to track and retarget the complete motion of a subject. Established techniques for full body motion capture like optical motion tracking exist. However, due to their computational complexity and their reliance on pre-specified models, they fail to meet the demanding requirements of virtual reality environments such as real-time response, immersion, and ad hoc configurability. Our goal is to support the use of motion capture as a general input device for virtual reality applications. In this paper we present a self-calibrating framework for optical motion capture, enabling the reconstruction and tracking of arbitrary articulated objects in real-time. Our method automatically estimates all relevant model parameters on-the-fly without any information on the initial tracking setup or the marker distribution, and computes the geometry and topology of multiple tracked skeletons. Moreover, we show how the model can make the motion capture phase robust against marker occlusions by exploiting the redundancy in the skeleton model and by reconstructing missing inner limbs and joints of the subject from partial information. Meeting the above requirements our system is well applicable to a wide range of virtual reality based applications, where unconstrained tracking and flexible retargeting of motion data is desirable.