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
Latest papers with no code
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.
BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions.
Multiple View Geometry Transformers for 3D Human Pose Estimation
In this work, we aim to improve the 3D reasoning ability of Transformers in multi-view 3D human pose estimation.
3DHR-Co: A Collaborative Test-time Refinement Framework for In-the-Wild 3D Human-Body Reconstruction Task
We answer this challenge by proposing a strategy that complements 3DHR test-time refinement work under a collaborative approach.
Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone
To address the issue of perspective ambiguity, we expand upon prior work by predicting the cameras' elevation angle relative to the subjects' pelvis.
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation
In this paper, we carry out in-depth analysis to understand to what degree the state-of-the-art disentangled representation learning methods truly separate the appearance information from the pose one.
GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation & Tracking in the Wild
An accurate and uncertainty-aware 3D human body pose estimation is key to enabling truly safe but efficient human-robot interactions.
TEMPO: Efficient Multi-View Pose Estimation, Tracking, and Forecasting
In doing so, our model is able to use spatiotemporal context to predict more accurate human poses without sacrificing efficiency.
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation
Furthermore, our method excels in accurately retrieving complete 3D poses even in the presence of occlusions, making it highly applicable in situations where complete 2D pose information is unavailable.
