Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.
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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
Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images.
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 Pose Estimation on ITOP front-view
This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image.
The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation.
#3 best model for Hand Pose Estimation on MSRA Hands
Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.