computer-vision-11

3D Hand Pose Estimation

3260 papers • 126 benchmarks • 313 datasets

Image: Zimmerman et l

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-hand-pose-estimation-23

Trend

Dataset

Best Model

Actions

HO-3D

H3WB

FreiHAND

Libraries

i

Use these libraries to find 3d-hand-pose-estimation-23 models and implementations

open-mmlab/mmpose

3 papers 4,932

Datasets

Subtasks

hand-object pose Grasp Generation 3D Canonical Hand Pose Estimation

Most implemented papers

End-to-End Recovery of Human Shape and Pose

Angjoo Kanazawa, Jitendra Malik, Michael J. Black, D. Jacobs•Sun Dec 17 2017

This work introduces an adversary trained to tell whether human body shape and pose parameters are real or not using a large database of 3D human meshes, and produces a richer and more useful mesh representation that is parameterized by shape and 3D joint angles.

1967

Content

DexYCB

InterHand2.6M

rongakowang/densemutualattention

2 papers 31

First-Person Hand Action Benchmark

EgoDexter

SynthHands

0

Paper Graph

Learning to Estimate 3D Hand Pose from Single RGB Images

T. Brox, Christiane Zimmermann•Tue May 02 2017

A deep network is proposed that learns a network-implicit 3D articulation prior that yields good estimates of the 3D pose from regular RGB images, and a large scale 3D hand pose dataset based on synthetic hand models is introduced.

774 0

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V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Kyoung Mu Lee, Gyeongsik Moon, Ju Yong Chang•Sun Nov 19 2017

This model is designed as a 3D CNN that provides accurate estimates while running in real-time and outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based3D hand pose estimation challenge.

451 0

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DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation

Markus Oberweger, V. Lepetit•Sun Aug 27 2017

With simple improvements: adding ResNet layers, data augmentation, and better initial hand localization, DeepPrior achieves better or similar performance than more sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA) while keeping the simplicity of the original method.

251 0

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Learning Joint Reconstruction of Hands and Manipulated Objects

Gül Varol, I. Laptev, Michael J. Black, C. Schmid, Dimitrios Tzionas, Yana Hasson, Igor Kalevatykh•Wed Apr 10 2019

This work presents an end-to-end learnable model that exploits a novel contact loss that favors phys- ically plausible hand-object constellations, and improves grasp quality metrics over baselines, using RGB images as input.

608 0

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3D Hand Shape and Pose From Images in the Wild

Philip H. S. Torr, A. Boukhayma, Rodrigo de Bem•Fri Feb 08 2019

This work presents the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild, consisting of the concatenation of a deep convolutional encoder, and a fixed model-based decoder.

393 0

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End-to-End Hand Mesh Recovery From a Monocular RGB Image

Xiong Zhang, Wen Zheng, Qiang Li, Wenbo Zhang•Sun Feb 24 2019

Qualitative experiments show that the HAMR framework is capable of recovering appealing 3D hand mesh even in the presence of severe occlusions, and outperforms the state-of-the-art methods for both 2D and3D hand pose estimation from a monocular RGB image on several benchmark datasets.

237 0

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3D Hand Shape and Pose Estimation From a Single RGB Image

Jianfei Cai, Junsong Yuan, Yuncheng Li, Liuhao Ge, Zhou Ren, Zehao Xue, Yingying Wang•Sat Mar 02 2019

This work proposes a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose and proposes a weakly-supervised approach by leveraging the depth map as a weak supervision in training.

487 0

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Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

M. Bronstein, S. Zafeiriou, Iasonas Kokkinos, R. Güler, Dominik Kulon•Fri Apr 03 2020

This work introduces 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 direct3D hand mesh reconstruction loss.

231 0

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Convolutional Mesh Regression for Single-Image Human Shape Reconstruction

Kostas Daniilidis, G. Pavlakos, Nikos Kolotouros•Tue May 07 2019

This paper addresses the problem of 3D human pose and shape estimation from a single image by proposing a graph-based mesh regression, which outperform the comparable baselines relying on model parameter regression, and achieves state-of-the-art results among model-based pose estimation approaches.

566 0

Paper Graph

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