3D Hand Pose Estimation
62 papers with code • 5 benchmarks • 16 datasets
Image: Zimmerman et l
Libraries
Use these libraries to find 3D Hand Pose Estimation models and implementationsDatasets
Most implemented papers
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image.
InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image
Therefore, we firstly propose (1) a large-scale dataset, InterHand2. 6M, and (2) a baseline network, InterNet, for 3D interacting hand pose estimation from a single RGB image.
HandTailor: Towards High-Precision Monocular 3D Hand Recovery
3D hand pose estimation and shape recovery are challenging tasks in computer vision.
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis
In contrast, data synthesis can easily ensure those diversities separately.
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
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.
Dense 3D Regression for Hand Pose Estimation
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.
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
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
Hand PointNet: 3D Hand Pose Estimation Using Point Sets
Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images.
3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
In this paper, we propose a novel method that seeks to predict the 3d position of the hand using both synthetic and partially-labeled real data.