3D Hand Pose Estimation
64 papers with code • 5 benchmarks • 16 datasets
Image: Zimmerman et l
Benchmarks
These leaderboards are used to track progress in 3D Hand Pose Estimation
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Libraries
Use these libraries to find 3D Hand Pose Estimation models and implementationsDatasets
FreiHAND
HO-3D
DexYCB
AGORA
InterHand2.6M
ExPose
3D Hand Pose
First-Person Hand Action Benchmark
EgoDexter
SynthHands
Most implemented papers
End-to-end Recovery of Human Shape and Pose
open-mmlab/mmpose
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CVPR 2018
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.
Learning to Estimate 3D Hand Pose from Single RGB Images
lmb-freiburg/hand3d
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ICCV 2017
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
mks0601/V2V-PoseNet_RELEASE
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CVPR 2018
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.
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
hassony2/manopth
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CVPR 2019
Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation.
3D Hand Shape and Pose from Images in the Wild
yihui-he/epipolar-transformers
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CVPR 2019
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
3d-hand-shape/hand-graph-cnn
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CVPR 2019
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.
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
674106399/JointBoneLoss
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CVPR 2020
We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss.
