Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.
( Image credit: Pose-REN )
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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.
Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images.
We present the first method to capture the 3D total motion of a target person from a monocular view input.
Ranked #33 on 3D Human Pose Estimation on Human3.6M
This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image.
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
Ranked #3 on Hand Pose Estimation on HANDS 2017
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.
Ranked #1 on Pose Estimation on ITOP front-view
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.