3D Human Pose Estimation
302 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
Exploring Latent Cross-Channel Embedding for Accurate 3D Human Pose Reconstruction in a Diffusion Framework
However, there is still ample room for improvement as these methods often overlook the exploration of correlation between the 2D and 3D joint-level features.
D3PRefiner: A Diffusion-based Denoise Method for 3D Human Pose Refinement
Additionally, we leverage the architecture of current diffusion models to convert the distribution of noisy 3D poses into ground truth 3D poses.
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.
