3D Hand Pose Estimation

65 papers with code • 5 benchmarks • 16 datasets


Use these libraries to find 3D Hand Pose Estimation models and implementations

Most implemented papers

End-to-end Recovery of Human Shape and Pose

open-mmlab/mmpose 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 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 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

moberweger/deep-prior-pp 28 Aug 2017

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 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 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

MandyMo/HAMR ICCV 2019

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 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

nkolot/GraphCMR CVPR 2019

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 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.