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DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth [article]

Jameel Malik, Ahmed Elhayek, Fabrizio Nunnari, Kiran Varanasi, Kiarash Tamaddon, Alexis Heloir, Didier Stricker
2018 arXiv   pre-print
Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation.  ...  In contrast to the existing methods which optimize only for joint positions, we propose a fully supervised deep network which learns to jointly estimate a full 3D hand mesh representation and pose from  ...  Related Work Depth-based hand pose estimation has been extensively studied in the computer vision community. We refer the reader to the survey [39] for a detailed overview of the field.  ... 
arXiv:1808.09208v1 fatcat:gwzkoqq6vne63gsxbrbbkisq74

A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods

Theocharis Chatzis, Andreas Stergioulas, Dimitrios Konstantinidis, Kosmas Dimitropoulos, Petros Daras
2020 Applied Sciences  
robust markerless 3D hand pose estimation methods.  ...  The field of 3D hand pose estimation has been gaining a lot of attention recently, due to its significance in several applications that require human-computer interaction (HCI).  ...  • We propose a new taxonomy for a better categorization and presentation of deep-learning-based 3D hand pose estimation methods.  ... 
doi:10.3390/app10196850 fatcat:hgyqkoyetbbilncksguarqz3bq

Cross-Modal Deep Variational Hand Pose Estimation

Adrian Spurr, Jie Song, Seonwook Park, Otmar Hilliges
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
The human hand moves in complex and highdimensional ways, making estimation of 3D hand pose configurations from images alone a challenging task.  ...  This latent space can be directly used to estimate 3D hand poses from RGB images, outperforming the state-of-the art in different settings.  ...  [34] attempt to learn a manifold of hand poses via a combination of variational autoencoders (VAEs) and generative adversarial networks (GANs) for hand pose estimation from depth images.  ... 
doi:10.1109/cvpr.2018.00017 dblp:conf/cvpr/Spurr0PH18 fatcat:3bghb65fv5fb7alh6zkjxr3l7y

Hand Pose Estimation: A Survey [article]

Bardia Doosti
2019 arXiv   pre-print
Hand Pose Estimation a hot topic in computer vision field.  ...  In this report, we will first explain the hand pose estimation problem and will review major approaches solving this problem, especially the two different problems of using depth maps or RGB images.  ...  Depth-based Methods Traditionally, depth map image based methods were the main method in hand and body pose estimation. Sinha et al.  ... 
arXiv:1903.01013v2 fatcat:rqkthrt4mjawlk4p2ij5nut64q

Survey on depth and RGB image-based 3D hand shape and pose estimation

Lin Huang, Boshen Zhang, Zhilin Guo, Yang Xiao, Zhiguo Cao, Junsong Yuan
2021 Virtual Reality & Intelligent Hardware  
hand shape and pose estimation.  ...  With the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks (DNNs), numerous DNN-based data-driven methods have been proposed for accurate and rapid  ...  and have led to significant advances in depth-based 3D hand pose estimation.  ... 
doi:10.1016/j.vrih.2021.05.002 fatcat:4tbhftt3ira6fporaqlscqhsse

A Survey on GAN-Based Data Augmentation for Hand Pose Estimation Problem

Farnaz Farahanipad, Mohammad Rezaei, Mohammad Sadegh Nasr, Farhad Kamangar, Vassilis Athitsos
2022 Technologies  
Deep learning solutions for hand pose estimation are now very reliant on comprehensive datasets covering diverse camera perspectives, lighting conditions, shapes, and pose variations.  ...  Next, we present related hand pose datasets and performance comparison of some of these methods for the hand pose estimation problem.  ...  The HPE is responsible for estimating the 3D hand pose based on the input depth map. During the training, these three networks are optimized to reduce the error of HPE.  ... 
doi:10.3390/technologies10020043 fatcat:6ljh7d4ijrfsdgigm5zn4lg54y

SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation [article]

John Yang, Hyung Jin Chang, Seungeui Lee, Nojun Kwak
2020 arXiv   pre-print
3D hand pose estimation based on RGB images has been studied for a long time. Most of the studies, however, have performed frame-by-frame estimation based on independent static images.  ...  We show that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in experiments on hand pose estimation  ...  To strictly imitate human perception of hand poses, it is critical for RGB-based hand pose estimators to understand the dynamics of pose variations in a spatio-temporal space.  ... 
arXiv:2007.05168v1 fatcat:ksmivqhqbnenxgfdtbcybowkiq

Cross-modal Deep Variational Hand Pose Estimation [article]

Adrian Spurr, Jie Song, Seonwook Park, Otmar Hilliges
2018 arXiv   pre-print
The human hand moves in complex and high-dimensional ways, making estimation of 3D hand pose configurations from images alone a challenging task.  ...  This latent space can be directly used to estimate 3D hand poses from RGB images, outperforming the state-of-the art in different settings.  ...  [34] attempt to learn a manifold of hand poses via a combination of variational autoencoders (VAEs) and generative adversarial networks (GANs) for hand pose estimation from depth images.  ... 
arXiv:1803.11404v1 fatcat:nqjhvoy42jgprcslw54oqggvhm

Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation [article]

Chengde Wan, Thomas Probst, Luc Van Gool, Angela Yao
2017 arXiv   pre-print
State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data.  ...  Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth maps.  ...  Hand pose estimation Hand pose estimation generally falls into two camps, i.e. model-based tracking and framewise discriminative estimation.  ... 
arXiv:1702.03431v2 fatcat:rhqacaskzvagxo4ofbwvg7fpku

Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation

Chengde Wan, Thomas Probst, Luc Van Gool, Angela Yao
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data.  ...  Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth map.  ...  Hand pose estimation Hand pose estimation generally falls into two camps, i.e. model-based tracking and framewise discriminative estimation.  ... 
doi:10.1109/cvpr.2017.132 dblp:conf/cvpr/WanPGY17 fatcat:e7kdf3o5efeinhtqjtjbfy4s5i

Disentangling Latent Hands for Image Synthesis and Pose Estimation [article]

Linlin Yang, Angela Yao
2019 arXiv   pre-print
Hand image synthesis and pose estimation from RGB images are both highly challenging tasks due to the large discrepancy between factors of variation ranging from image background content to camera viewpoint  ...  Experiments show that our dVAE can synthesize highly realistic images of the hand specifiable by both pose and image background content and also estimate 3D hand poses from RGB images with accuracy competitive  ...  Related Works Hand Pose Estimation Much of the progress made in hand pose estimation have focused on using depth image inputs [5, 6, 7, 8, 11, 14, 15, 18, 19, 32, 33, 35] .  ... 
arXiv:1812.01002v2 fatcat:sdyhok5mnvftjismaouowlrh7y

Disentangling Latent Hands for Image Synthesis and Pose Estimation

Linlin Yang, Angela Yao
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Hand image synthesis and pose estimation from RGB images are both highly challenging tasks due to the large discrepancy between factors of variation ranging from image background content to camera viewpoint  ...  Experiments show that our dVAE can synthesize highly realistic images of the hand specifiable by both pose and image background content and also estimate 3D hand poses from RGB images with accuracy competitive  ...  Related Works Hand Pose Estimation Much of the progress made in hand pose estimation have focused on using depth image inputs [5, 6, 7, 8, 11, 14, 15, 18, 19, 32, 33, 35] .  ... 
doi:10.1109/cvpr.2019.01011 dblp:conf/cvpr/YangY19 fatcat:aw2mdzcsmvbs3jqgtyqfursyiu

A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation [article]

Xiuyuan Li, Jiangang Yu, Fengchao Li, Guowen An
2022 arXiv   pre-print
To solve the ill-posed problem of depth estimation, a self-supervised multi-task learning aided depth estimation module is designed based on the strong correlation between the depth estimation and transmission  ...  The experimental results on the synthetic foggy KITTI dataset show that the proposed self-supervised monocular VO performs better in depth and pose estimation than other state-of-the-art monocular VO in  ...  On the other hand, the depth estimation network of the baseline is just a common encoder-decoder, which only contains one decoder for the depth estimation, thus losing the promotion from transmission map  ... 
arXiv:2203.04812v1 fatcat:2aqd4rn6pjcrhmozaj3gfsx2hq

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Munir Oudah, Ali Al-Naji, Javaan Chahl
2020 Journal of Imaging  
Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision.  ...  This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.  ...  Acknowledgments: The authors would like to thank the staff in Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq and the participants for their support to conduct the  ... 
doi:10.3390/jimaging6080073 pmid:34460688 fatcat:zmid23k67vbozb54sfji4nlfiy

MM-Hand: 3D-Aware Multi-Modal Guided Hand Generative Network for 3D Hand Pose Synthesis [article]

Zhenyu Wu, Duc Hoang, Shih-Yao Lin, Yusheng Xie, Liangjian Chen, Yen-Yu Lin, Zhangyang Wang, Wei Fan
2020 arXiv   pre-print
Instead, we have developed a learning-based approach to synthesize realistic, diverse, and 3D pose-preserving hand images under the guidance of 3D pose information.  ...  Estimating the 3D hand pose from a monocular RGB image is important but challenging. A solution is training on large-scale RGB hand images with accurate 3D hand keypoint annotations.  ...  [35] proposes a combination of surface-based pose estimation and deep generative models to perform accurate pose transfer. Siarohin et al.  ... 
arXiv:2010.01158v1 fatcat:j42oeeqlanct7mtwgozazmnk6e
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