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- W2806874516 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." @default.
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- W2806874516 date "2017-10-01" @default.
- W2806874516 modified "2023-10-16" @default.
- W2806874516 title "How to Refine 3D Hand Pose Estimation from Unlabelled Depth Data?" @default.
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- W2806874516 doi "https://doi.org/10.1109/3dv.2017.00025" @default.
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