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Found 8 papers, 3 papers with code
End-to-End 3D Hand Pose Estimation from Stereo Cameras
This work proposes an end-to-end approach to estimate full 3D hand pose from stereo cameras.
3D Hand Shape and Pose Estimation from a Single RGB Image
This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image.
Point-to-Point Regression PointNet for 3D Hand Pose Estimation
Convolutional Neural Networks (CNNs)-based methods for 3D hand pose estimation with depth cameras usually take 2D depth images as input and directly regress holistic 3D hand pose.
Weakly-supervised 3D Hand Pose Estimation from Monocular RGB Images
Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D hand pose from monocular RGB images, due to substantial depth ambiguity and the difficulty of obtaining fully-annotated training data.
Hand PointNet: 3D Hand Pose Estimation Using Point Sets
Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images.
Ranked #7 on
Hand Pose Estimation
on HANDS 2017
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
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
Ranked #5 on
Hand Pose Estimation
on HANDS 2017
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images
We propose a simple, yet effective approach for real-time hand pose estimation from single depth images using three-dimensional Convolutional Neural Networks (3D CNNs).
Robust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNs
Articulated hand pose estimation plays an important role in human-computer interaction.
