16590

Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit
Institute for Computer Graphics and Vision, Graz University of Technology, Austria
arXiv:1609.09698 [cs.CV], (30 Sep 2016)

@article{oberweger2016training,

   title={Training a Feedback Loop for Hand Pose Estimation},

   author={Oberweger, Markus and Wohlhart, Paul and Lepetit, Vincent},

   year={2016},

   month={sep},

   archivePrefix={"arXiv"},

   primaryClass={cs.CV}

}

We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. They remove the need for fitting a 3D model to the input data, which requires both a carefully designed fitting function and algorithm. We show that our approach outperforms state-of-the-art methods, and is efficient as our implementation runs at over 400 fps on a single GPU.
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