Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.
|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
#4 best model for Image-to-Image Translation on Cityscapes Photo-to-Labels
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
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.
SOTA for Hand Pose Estimation on ICVL Hands
DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map.
#4 best model for Hand Pose Estimation on MSRA Hands
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.
SOTA for Hand Pose Estimation on MSRA Hands
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 pose estimation from single depth image is an important and challenging problem for human-computer interaction.
Hand pose estimation from monocular depth images is an important and challenging problem for human-computer interaction.
#4 best model for Hand Pose Estimation on ICVL Hands