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Hand PointNet: 3D Hand Pose Estimation Using Point Sets [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images. Different from existing CNN-based hand pose estimation methods that take either 2D images or 3D volumes as the input, our proposed Hand PointNet ...
Junwu Weng, Yujun Cai, Junsong Yuan
exaly   +6 more sources

Recurrent 3D Hand Pose Estimation Using Cascaded Pose-Guided 3D Alignments

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
3D hand pose estimation is a challenging problem in computer vision due to the high degrees-of-freedom of hand articulated motion space and large viewpoint variation.
Yinda Zhang, Xiaoming Deng, Ping Tan
exaly   +2 more sources

Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation [PDF]

Neurocomputing, 2020
Accepted by ...
Cairong Zhang, Xinghao Chen, Guijin Wang
exaly   +5 more sources

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Human hand identification with 3D hand pose variations

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2010
Several recent research efforts in the hand biometrics have focused on developing contact-free personal identification system. However, the acquisition of images from the unconstrained hands can introduce significant hand pose variations which severely limits the performance and applicability of these approaches.
Vivek Kanhangad, Ajay Kumar 0001, David Zhang 0001   +2 more
openaire   +1 more source

Finding the pose of an object in a hand

Proceedings. 1991 IEEE International Conference on Robotics and Automation, 2002
The author addresses the problem of finding the pose of an object grasped by a hand. The problem can be stated simply: given a hand grasping an object, and given a model of that object, determine the position and orientation of the object with respect to the hand.
openaire   +2 more sources

A Novel Framework of Hand Localization and Hand Pose Estimation

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
In this paper, we propose a novel framework for hand localization and pose estimation from a single depth image. For hand localization, unlike most existing methods that using heuristic strategies, e.g. color segmentation, we propose Hierarchical Hand location Networks (HHLN) to estimate the hand location from coarse to fine in depth images, which is ...
Yunlong Che, Yuxiang Song, Yue Qi
openaire   +2 more sources

Finger recognition for hand pose determination

2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
Hand pose and gesture recognition methods have been based on many properties of hand images, but using finger recognition is unique. Here we describe hand pose from finger recognition, its limitations and its advantages. The implementation is being used for some real applications, including interfaces and computer games, and has a success rate of over ...
J. R. Parker 0001, Mark R. Baumback
openaire   +1 more source

First-Person Hand Action Benchmark With RGB-D Videos and 3D Hand Pose Annotations

, 2018
In this work we study the use of 3D hand poses to recognize first-person dynamic hand actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories ...
Tae-Kyun Kim, Guillermo García-Hernando
exaly   +2 more sources

A CRNN module for hand pose estimation

Neurocomputing, 2019
Abstract Hand pose estimation plays an important role in human–computer interaction. The traditional way is to deal with a video stream frame by frame. However, since the gesture in the video is changing continuously, the adjacent frames must be highly related to each other.
Zhongxu Hu, Youmin Hu, Jie Liu 0017, Bo Wu 0006, Dongmin Han, Thomas R. Kurfess   +5 more
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Inductive learning in hand pose recognition

Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, 2002
Hand pose recognition for gestural systems must be able to handle variations in user hand anatomy, perspective effects, and the idiosyncrasies of individual gesture presentation. We present an inductive learning system that is able to derive a rulebase of disjunctive normal form formulae in which each DNF describes a hand pose, and each conjunct within
Francis K. H. Quek, Meide Zhao
openaire   +1 more source