Hand Pose Estimation
87 papers with code • 10 benchmarks • 22 datasets
Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.
( Image credit: Pose-REN )
Benchmarks
These leaderboards are used to track progress in Hand Pose Estimation
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Libraries
Use these libraries to find Hand Pose Estimation models and implementationsDatasets
3DPW
RHD
InterHand2.6M
COCO-WholeBody
ICVL
NYU Hand
MSRA Hand
First-Person Hand Action Benchmark
EgoDexter
Most implemented papers
Model-based Deep Hand Pose Estimation
For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation.
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.
Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation
The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation.
Dense 3D Regression for Hand Pose Estimation
melonwan/denseReg
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CVPR 2018
Specifically, we decompose the pose parameters into a set of per-pixel estimations, i. e., 2D heat maps, 3D heat maps and unit 3D directional vector fields.
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
mks0601/V2V-PoseNet_RELEASE
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CVPR 2018
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
Cross-modal Deep Variational Hand Pose Estimation
spurra/vae-hands-3d
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CVPR 2018
Furthermore, we show that our proposed method can be used without changes on depth images and performs comparably to specialized methods.
Hand PointNet: 3D Hand Pose Estimation Using Point Sets
3huo/Hand-Pointnet
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CVPR 2018
Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images.
3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
masabdi/LSPS
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British Machine Vision Conference 2018 2018
In this paper, we propose a novel method that seeks to predict the 3d position of the hand using both synthetic and partially-labeled real data.
SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond
KevinKecc/SRN
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The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the object ground-truth symmetry and the side-outputs of multiple stages.
MURAUER: Mapping Unlabeled Real Data for Label AUstERity
poier/murauer
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In this work, we remove this requirement by learning to map from the features of real data to the features of synthetic data mainly using a large amount of synthetic and unlabeled real data.
