Skeletal Graph Based Human Pose Estimation in Real-Time

We propose a new method to quickly and robustly estimate the 3D pose of the human skeleton from volumetric body scans without the need for visual markers. The core principle of our algorithm is to apply a fast center-line extraction to 3D voxel data and robustly fit a skeleton model to the resulting graph. Our algorithm allows for automatic, single-frame initialization and tracking of the human pose while being fast enough for real-time applications at up to 30 frames per second. We provide an extensive qualitative and quantitative evaluation of our method on real and synthetic datasets which demonstrates the stability of our algorithm even when applied to long motion sequences.

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