Improving 3D Hand Pose Estimation with Synthetic RGB Image Enhancement Using RetinexNet and Dehazing

2021 ◽  
pp. 93-105
Author(s):  
Alysa Tan ◽  
Bryan Kwek ◽  
Kenneth Anthony ◽  
Vivian Teh ◽  
Yifan Yang ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Rgb Image ◽  
Hand Pose

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

2020 ◽  
Vol 10 (2) ◽  
pp. 618
Author(s):  
Xianghan Wang ◽  
Jie Jiang ◽  
Yanming Guo ◽  
Lai Kang ◽  
Yingmei Wei ◽  
...  
Keyword(s):  
Computer Vision ◽  
Pose Estimation ◽  
Spatial Information ◽  
Image Features ◽  
Estimation Methods ◽  
Feature Maps ◽  
Hand Pose Estimation ◽  
Rgb Images ◽  
Rgb Image ◽  
Hand Pose

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.

scholarly journals Cascaded Hierarchical CNN for RGB-Based 3D Hand Pose Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shiming Dai ◽  
Wei Liu ◽  
Wenji Yang ◽  
Lili Fan ◽  
Jihao Zhang
Keyword(s):  
Pose Estimation ◽  
Depth Image ◽  
Estimation Methods ◽  
Hierarchical Network ◽  
Human Machine Interaction ◽  
Depth Cameras ◽  
Hand Pose Estimation ◽  
Public Datasets ◽  
Rgb Image ◽  
Hand Pose

3D hand pose estimation can provide basic information about gestures, which has an important significance in the fields of Human-Machine Interaction (HMI) and Virtual Reality (VR). In recent years, 3D hand pose estimation from a single depth image has made great research achievements due to the development of depth cameras. However, 3D hand pose estimation from a single RGB image is still a highly challenging problem. In this work, we propose a novel four-stage cascaded hierarchical CNN (4CHNet), which leverages hierarchical network to decompose hand pose estimation into finger pose estimation and palm pose estimation, extracts separately finger features and palm features, and finally fuses them to estimate 3D hand pose. Compared with direct estimation methods, the hand feature information extracted by the hierarchical network is more representative. Furthermore, concatenating various stages of the network for end-to-end training can make each stage mutually beneficial and progress. The experimental results on two public datasets demonstrate that our 4CHNet can significantly improve the accuracy of 3D hand pose estimation from a single RGB image.

scholarly journals InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image

2020 ◽  
pp. 548-564
Author(s):  
Gyeongsik Moon ◽  
Shoou-I Yu ◽  
He Wen ◽  
Takaaki Shiratori ◽  
Kyoung Mu Lee
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Rgb Image ◽  
Hand Pose

Occlusion-Robust 3D Hand Pose Estimation from a Single RGB Image

2021 ◽  
Author(s):  
Asuka Ishii ◽  
Gaku Nakano ◽  
Tetsuo Inoshita
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Rgb Image ◽  
Hand Pose

scholarly journals Hand Pose Estimation in the Task of Egocentric Actions

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 10533-10547
Author(s):  
Marek Hruz ◽  
Jakub Kanis ◽  
Zdenek Krnoul
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals 3D Hand Pose Estimation via Graph-Based Reasoning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 35824-35833
Author(s):  
Jae-Hun Song ◽  
Suk-Ju Kang
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand PointNet-based 3D Hand Pose Estimation in Egocentric RGB-D Images

2020 ◽  
Author(s):  
Van-Hung Le ◽  
Van-Nam Hoang ◽  
Hai Vu ◽  
Thi-Lan Le ◽  
Thanh-Hai Tran ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1007
Author(s):  
Chi Xu ◽  
Yunkai Jiang ◽  
Jun Zhou ◽  
Yi Liu
Keyword(s):  
Gesture Recognition ◽  
Pose Estimation ◽  
Color Image ◽  
Recognition Performance ◽  
Recognition Task ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Estimation Task ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.

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