Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit

ICCV 2015

doi:10.1109/ICCV.2015.379 /iccv/2015/oberweger2015iccv-training/

Abstract

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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Cite

Text

Oberweger et al. "Training a Feedback Loop for Hand Pose Estimation." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.379

Markdown

[Oberweger et al. "Training a Feedback Loop for Hand Pose Estimation." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/oberweger2015iccv-training/) doi:10.1109/ICCV.2015.379

BibTeX

@inproceedings{oberweger2015iccv-training,
  title     = {{Training a Feedback Loop for Hand Pose Estimation}},
  author    = {Oberweger, Markus and Wohlhart, Paul and Lepetit, Vincent},
  booktitle = {International Conference on Computer Vision},
  year      = {2015},
  doi       = {10.1109/ICCV.2015.379},
  url       = {https://mlanthology.org/iccv/2015/oberweger2015iccv-training/}
}