- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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 Show more
Book title2017 International Conference on 3D Vision (3DV)
Pages / Article No.
Subjecthand tracking, pose estimation, deep learning
Organisational unit03420 - Gross, Markus
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