DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
Markus Oberweger,Vincent Lepetit +1 more
- pp 585-594
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TLDR
Weger et al. as discussed by the authors proposed to add ResNet layers, data augmentation, and better initial hand localization, achieving better or similar performance than more sophisticated recent methods on the three main benchmarks.Abstract:
DeepPrior [18] is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map. Since its publication early 2015, it has been outperformed by several impressive works. Here we show that with simple improvements: adding ResNet layers, data augmentation, and better initial hand localization, we achieve better or similar performance than more sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA) while keeping the simplicity of the original method. Our new implementation is available at https://github.com/moberweger/deep-prior-pp.read more
Citations
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Proceedings ArticleDOI
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
TL;DR: This model is designed as a 3D CNN that provides accurate estimates while running in real-time and outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based3D hand pose estimation challenge.
Proceedings ArticleDOI
Cross-Modal Deep Variational Hand Pose Estimation
TL;DR: This work proposes a method to learn a statistical hand model represented by a cross-modal trained latent space via a generative deep neural network, which can be directly used to estimate 3D hand poses from RGB images, outperforming the state-of-the art in different settings.
Proceedings ArticleDOI
Hand PointNet: 3D Hand Pose Estimation Using Point Sets
TL;DR: The proposed Hand PointNet directly processes the 3D point cloud that models the visible surface of the hand for pose regression, and takes the normalized point cloud as the input to capture complex hand structures and accurately regress a low dimensional representation of the3D hand pose.
Proceedings ArticleDOI
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Shanxin Yuan,Guillermo Garcia-Hernando,Bjorn Stenger,Gyeongsik Moon,Ju Yong Chang,Kyoung Mu Lee,Pavlo Molchanov,Jan Kautz,Sina Honari,Liuhao Ge,Junsong Yuan,Xinghao Chen,Guijin Wang,Fan Yang,Kai Akiyama,Yang Wu,Qingfu Wan,Meysam Madadi,Sergio Escalera,Shile Li,Dongheui Lee,Iason Oikonomidis,Antonis A. Argyros,Tae-Kyun Kim +23 more
TL;DR: In this paper, the state-of-the-art 3D hand pose estimation from depth images is investigated, and the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions.
Proceedings ArticleDOI
Using a Single RGB Frame for Real Time 3D Hand Pose Estimation in the Wild
TL;DR: This work capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D hand Pose estimation in unrestricted scenarios.
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