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Open AccessArticle

CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention

1
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
2
Graduate College, National University of Defense Technology, Changsha 410073, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 618; https://doi.org/10.3390/app10020618
Received: 28 November 2019 / Revised: 9 January 2020 / Accepted: 10 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Advanced Ultrafast Imaging)
Precise 3D hand pose estimation can be used to improve the performance of human–computer interaction (HCI). Specifically, computer-vision-based hand pose estimation can make this process more natural. Most traditional computer-vision-based hand pose estimation methods use depth images as the input, which requires complicated and expensive acquisition equipment. Estimation through a single RGB image is more convenient and less expensive. Previous methods based on RGB images utilize only 2D keypoint score maps to recover 3D hand poses but ignore the hand texture features and the underlying spatial information in the RGB image, which leads to a relatively low accuracy. To address this issue, we propose a channel fusion attention mechanism that combines 2D keypoint features and RGB image features at the channel level. In particular, the proposed method replans weights by using cascading RGB images and 2D keypoint features, which enables rational planning and the utilization of various features. Moreover, our method improves the fusion performance of different types of feature maps. Multiple contrast experiments on public datasets demonstrate that the accuracy of our proposed method is comparable to the state-of-the-art accuracy. View Full-Text
Keywords: hand pose estimation; CFAM; 3D keypoint; RGB image; attention hand pose estimation; CFAM; 3D keypoint; RGB image; attention
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Wang, X.; Jiang, J.; Guo, Y.; Kang, L.; Wei, Y.; Li, D. CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention. Appl. Sci. 2020, 10, 618.

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