3D convolutional neural networks for efficient and robust hand pose estimation from single depth images
We propose a simple, yet effective approach for real-time hand pose estimation from single depth images using three-dimensional Convolutional Neural Networks (3D CNNs). Image based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial infor...
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Main Authors: | Ge, Liuhao, Liang, Hui, Yuan, Junsong, Thalmann, Daniel |
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Other Authors: | Interdisciplinary Graduate School (IGS) |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/82836 http://hdl.handle.net/10220/50409 |
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Institution: | Nanyang Technological University |
Language: | English |
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