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@InProceedings{Dibra2017c,
  Title                    = {How to Refine 3D Hand Pose Estimation from Unlabelled Depth Data ?},
  Author                   = {Endri Dibra and
Thomas Wolf and
 A. Cengiz {\"{O}}ztireli and
 Markus H. Gross},
  Booktitle                = {Fifth International Conference on 3D Vision (3DV), Qingdao, China, October 10-12, 2017},
  Year                     = {2017},

  Abstract                 = {Data-driven approaches for hand pose estimation from depth images usually require a substantial amount of labelled training data which is quite hard to obtain. In this work, we show how a simple convolutional neural network, pre-trained only on synthetic depth images generated from a single 3D hand model, can be trained to adapt to unlabelled depth images from a real user’s hand. We validate our method on two existing and a new dataset that we capture, both quantitatively and qualitatively, demonstrating that we strongly compare to state-of-the-art methods. Additionally, this method can be seen as an extension to existing methods trained on limited datasets, which helps on boosting their performance on new ones.}
}