Öztireli, A. Cengiz
- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
CNN-based approaches are typically data-hungry, and when the task to solve is monocular RGB hand pose inference, obtaining real labelled training data is very hard to obtain. To overcome this, in this work we propose a new, large, realistically rendered, available hand dataset and a neural network trained on it, with the ability to refine itself to real unlabeled RGB images, given unlabeled corresponding depth images. We benchmark and validate our method on available and captured datasets, demonstrating that we strongly compare and even outperform state-of-the-art methods on tasks varying from 3D pose estimation to hand gesture recognition. Show more
Book title2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Pages / Article No.
Organisational unit03420 - Gross, Markus / Gross, Markus
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