Hand Pose Estimation
87 papers with code • 10 benchmarks • 22 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Hand Pose Estimation
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Libraries
Use these libraries to find Hand Pose Estimation models and implementationsDatasets
3DPW
RHD
InterHand2.6M
COCO-WholeBody
ICVL
NYU Hand
MSRA Hand
First-Person Hand Action Benchmark
EgoDexter
Most implemented papers
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
674106399/JointBoneLoss
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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.
Whole-Body Human Pose Estimation in the Wild
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image
open-mmlab/mmpose
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ECCV 2020
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
NVlabs/dex-ycb-toolkit
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CVPR 2021
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking
In this work, we introduce ROFT, a Kalman filtering approach for 6D object pose and velocity tracking from a stream of RGB-D images.
HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud
cwc1260/handdiff
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Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications.
Hands Deep in Deep Learning for Hand Pose Estimation
We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map.
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation
In this paper, a hybrid hand pose estimation method is proposed by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method by a spatial attention mechanism and to the optimization of the generative method by hierarchical Particle Swarm Optimization (PSO).
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked so far.
