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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
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. 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 new automatically
annotated and augmented dataset. Our inside-in method captures full-body motion
in general indoor and outdoor scenes, and also crowded scenes.
