Weakly-supervised 3D Human Pose Estimation
18 papers with code • 2 benchmarks • 2 datasets
This task targets at 3D Human Pose Estimation with fewer 3D annotation.
Benchmarks
These leaderboards are used to track progress in Weakly-supervised 3D Human Pose Estimation
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Libraries
Use these libraries to find Weakly-supervised 3D Human Pose Estimation models and implementations
Human3.6M
MPI-INF-3DHP
Most implemented papers
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
DenisTome/Lifting-from-the-Deep-release
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CVPR 2017
We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks.
3D human pose estimation in video with temporal convolutions and semi-supervised training
facebookresearch/VideoPose3D
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CVPR 2019
We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back-project to the input 2D keypoints.
End-to-end Recovery of Human Shape and Pose
open-mmlab/mmpose
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CVPR 2018
The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations.
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
hrhodin/UnsupervisedGeometryAwareRepresentationLearning
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ECCV 2018
In this paper, we propose to overcome this problem by learning a geometry-aware body representation from multi-view images without annotations.
Heuristic Weakly Supervised 3D Human Pose Estimation
However, recent models depend on supervised training with 3D pose ground truth data or known pose priors for their target domains.
Self-supervised Learning of Motion Capture
chingswy/HumanPoseMemo
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NeurIPS 2017
In this work, we propose a learning based motion capture model for single camera input.
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation
bastianwandt/RepNet
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CVPR 2019
This efficiently avoids a simple memorization of the training data and allows for a weakly supervised training.
Self-Supervised Learning of 3D Human Pose using Multi-view Geometry
mkocabas/EpipolarPose
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CVPR 2019
Training accurate 3D human pose estimators requires large amount of 3D ground-truth data which is costly to collect.
TexturePose: Supervising Human Mesh Estimation with Texture Consistency
geopavlakos/TexturePose
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ICCV 2019
Assuming that the texture of the person does not change dramatically between frames, we can apply a novel texture consistency loss, which enforces that each point in the texture map has the same texture value across all frames.
Cascaded deep monocular 3D human pose estimation with evolutionary training data
Nicholasli1995/EvoSkeleton
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CVPR 2020
End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data.
