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キーワード:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
要旨:
We propose the first real-time approach for the egocentric estimation of 3D
human body pose in a wide range of unconstrained everyday activities. This
setting has a unique set of challenges, such as mobility of the hardware setup,
and robustness to long capture sessions with fast recovery from tracking
failures. We tackle these challenges based on a novel lightweight setup that
converts a standard baseball cap to a device for high-quality pose estimation
based on a single cap-mounted fisheye camera. From the captured egocentric live
stream, our CNN based 3D pose estimation approach runs at 60Hz on a
consumer-level GPU. In addition to the novel hardware setup, our other main
contributions are: 1) a large ground truth training corpus of top-down fisheye
images and 2) a novel disentangled 3D pose estimation approach that takes the
unique properties of the egocentric viewpoint into account. As shown by our
evaluation, we achieve lower 3D joint error as well as better 2D overlay than
the existing baselines.
