Abstract
In this work, we address the problem of recovering the 3D full-body human pose from depth images. A graph-based representation of the 3D point cloud data is determined which allows for the measurement of pose-independent geodesic distances on the surface of the human body. We extend previous approaches based on geodesic distances by extracting geodesic paths to multiple surface points which are obtained by adapting a 3D torso model to the point cloud data. This enables us to distinguish between the different body parts - without having to make prior assumptions about their locations. Subsequently, a kinematic skeleton model is adapted. Our method does not need any pre-trained pose classifiers and can therefore estimate arbitrary poses.
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Handrich, S., Al-Hamadi, A. (2015). Full-Body Human Pose Estimation by Combining Geodesic Distances and 3D-Point Cloud Registration. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_25
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DOI: https://doi.org/10.1007/978-3-319-25903-1_25
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