3D Human Pose Estimation
307 papers with code • 25 benchmarks • 46 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
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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
3D Human Mesh Regression with Dense Correspondence
zengwang430521/DecoMR
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CVPR 2020
This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i. e. a 2D space used for texture mapping of 3D mesh).
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation
Jeff-sjtu/HybrIK
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CVPR 2021
We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods.
3D Human Pose Estimation with Spatial and Temporal Transformers
zczcwh/PoseFormer
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ICCV 2021
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation.
Human Pose Regression with Residual Log-likelihood Estimation
Jeff-sjtu/res-loglikelihood-regression
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ICCV 2021
In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution.
TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments
Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current methods still cannot reliably estimate moving humans in global coordinates, which is critical for many applications.
Towards Viewpoint Invariant 3D Human Pose Estimation
mks0601/V2V-PoseNet_RELEASE
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We propose a viewpoint invariant model for 3D human pose estimation from a single depth image.
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Jtoo/fitting_human_smpl_model
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We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints.
Learning from Synthetic Humans
gulvarol/surreal
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CVPR 2017
In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.
Unite the People: Closing the Loop Between 3D and 2D Human Representations
MandyMo/pytorch_HMR
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CVPR 2017
With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.
Integral Human Pose Regression
JimmySuen/integral-human-pose
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ECCV 2018
State-of-the-art human pose estimation methods are based on heat map representation.
