3D Hand Pose Estimation
48 papers with code • 3 benchmarks • 14 datasets
Image: Zimmerman et l
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Most implemented papers
Learning to Estimate 3D Hand Pose from Single RGB Images
Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images.
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint.
End-to-end Recovery of Human Shape and Pose
The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations.
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map.
Learning joint reconstruction of hands and manipulated objects
Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation.
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
3D Hand Shape and Pose from Images in the Wild
We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild.
End-to-end Hand Mesh Recovery from a Monocular RGB Image
In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the problem of reconstructing the full 3D mesh of a human hand from a single RGB image.
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.
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location.