3D Human Pose Estimation
305 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
Latest papers with no code
3D Human Pose Perception from Egocentric Stereo Videos
Hence, existing methods often fail to accurately estimate complex 3D poses from egocentric views.
Lifting by Image -- Leveraging Image Cues for Accurate 3D Human Pose Estimation
In the second stage, we allow the keypoints to further emphasize the retained critical image features.
STRIDE: Single-video based Temporally Continuous Occlusion Robust 3D Pose Estimation
This challenge arises because these models struggle to generalize beyond their training datasets, and the variety of occlusions is hard to capture in the training data.
SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation
While recent two-stage many-to-one deep learning models have demonstrated great success in 3D human pose estimation, such models are inefficient ways to detect 3D key points in a sequential video relative to one-shot and many-to-many models.
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
Monocular 3D human pose estimation (3D-HPE) is an inherently ambiguous task, as a 2D pose in an image might originate from different possible 3D poses.
PointVoxel: A Simple and Effective Pipeline for Multi-View Multi-Modal 3D Human Pose Estimation
We fill this gap by introducing a pipeline called PointVoxel that fuses multi-view RGB and pointcloud inputs to obtain 3D human poses.
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input
We observe the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue.
Multi-View Person Matching and 3D Pose Estimation with Arbitrary Uncalibrated Camera Networks
The 2D human poses used in clustering are obtained through a pre-trained 2D pose detector, so our method does not require expensive 3D training data for each new scene.
RSB-Pose: Robust Short-Baseline Binocular 3D Human Pose Estimation with Occlusion Handling
This perception is injected by the Pose Transformer network and learned through a pre-training task that recovers iterative masked joints.
UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning
In this paper, we propose UniHPE, a unified Human Pose Estimation pipeline, which aligns features from all three modalities, i. e., 2D human pose estimation, lifting-based and image-based 3D human pose estimation, in the same pipeline.
