  Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor scenes with controlled backgrounds. Alternative
suit-based systems use several inertial measurement units or an exoskeleton to
capture motion. This makes capturing independent of a confined volume, but
requires substantial, often constraining, and hard to set up body
instrumentation. We therefore propose a new method for real-time, marker-less
and egocentric motion capture which estimates the full-body skeleton pose from
a lightweight stereo pair of fisheye cameras that are attached to a helmet or
virtual reality headset. It combines the strength of a new generative pose
estimation framework for fisheye views with a ConvNet-based body-part detector
trained on a large new dataset. Our inside-in method captures full-body motion
in general indoor and outdoor scenes, and also crowded scenes with many people
in close vicinity. The captured user can freely move around, which enables
reconstruction of larger-scale activities and is particularly useful in virtual
reality to freely roam and interact, while seeing the fully motion-captured
virtual body.
