Monocular RGB Hand Pose Inference from Unsupervised Refinable Nets
| Autor: | Cengiz Oztireli, Silvan Melchior, Endri Dibra, Thomas Wolf, Ali Balkis, Markus Gross |
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| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
business.industry Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inference 020207 software engineering Pattern recognition 02 engineering and technology Image segmentation 3D pose estimation Gesture recognition 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence business Pose |
| Zdroj: | CVPR Workshops |
| DOI: | 10.1109/cvprw.2018.00155 |
| Popis: | 3D hand pose inference from monocular RGB data is a challenging problem. CNN-based approaches have shown great promise in tackling this problem. However, such approaches are data-hungry, and obtaining real labeled training hand data is very hard. To overcome this, in this work, we propose a new, large, realistically rendered hand dataset and a neural network trained on it, with the ability to refine itself unsupervised on real unlabeled RGB images, given corresponding depth images. We benchmark and validate our method on existing and captured datasets, demonstrating that we strongly compare to or outperform state-of-the-art methods for various tasks ranging from 3D pose estimation to hand gesture recognition. |
| Databáze: | OpenAIRE |
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