3D Human Pose Estimation
312 papers with code • 25 benchmarks • 47 datasets
3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.
Benchmarks
These leaderboards are used to track progress in 3D Human Pose Estimation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find 3D Human Pose Estimation models and implementationsDatasets
Human3.6M
Waymo Open Dataset
3DPW
AMASS
MPI-INF-3DHP
DensePose
LSP
Panoptic
AGORA
TotalCapture
Subtasks
Most implemented papers
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature.
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
hrhodin/UnsupervisedGeometryAwareRepresentationLearning
•
•
ECCV 2018
In this paper, we propose to overcome this problem by learning a geometry-aware body representation from multi-view images without annotations.
BodyNet: Volumetric Inference of 3D Human Body Shapes
gulvarol/bodynet
•
•
ECCV 2018
Human shape estimation is an important task for video editing, animation and fashion industry.
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
mohomran/neural_body_fitting
•
•
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models.
3D Human Pose Machines with Self-supervised Learning
chanyn/3Dpose_ssl
•
•
arXiv.org 2019
Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests.
Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network
chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network
•
•
CVPR 2019
We argue that 3D human pose estimation from a monocular input is an inverse problem where multiple feasible solutions can exist.
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location.
Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation
Arthur151/DSD-SATN
•
•
ICCV 2019
Different from the existing methods try to obtain all the complex 3D pose, shape, and camera parameters from one coupling feature, we propose a skeleton-disentangling based framework, which divides this task into multi-level spatial and temporal granularity in a decoupling manner.
TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style
chaitanya100100/TailorNet
•
•
CVPR 2020
While the low-frequency component is predicted from pose, shape and style parameters with an MLP, the high-frequency component is predicted with a mixture of shape-style specific pose models.
VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
microsoft/voxelpose-pytorch
•
•
ECCV 2020
In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimations, we present an end-to-end solution which directly operates in the $3$D space, therefore avoids making incorrect decisions in the 2D space.
