How to Refine 3D Hand Pose Estimation from Unlabelled Depth Data ?
E. Dibra, T. Wolf, C. Öztireli, M. Gross
Proceedings of the Fifth International Conference on 3D Vision, 3DV (Qingdao, China, October 10-12, 2017), pp. 135-144
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.
