3D Human Pose Estimation
303 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
EgoPoseFormer: A Simple Baseline for Egocentric 3D Human Pose Estimation
We also show that our method can be seamlessly extended to monocular settings, which achieves state-of-the-art performance on the SceneEgo dataset, improving MPJPE by 25. 5mm (21% improvement) compared to the best existing method with only 60. 7% model parameters and 36. 4% FLOPs.
Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset
The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users.
PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery
With the recent advancements in single-image-based human mesh recovery, there is a growing interest in enhancing its performance in certain extreme scenarios, such as occlusion, while maintaining overall model accuracy.
Self-learning Canonical Space for Multi-view 3D Human Pose Estimation
To facilitate the aggregation of the intra- and inter-view, we define a canonical parameter space, depicted by per-view camera pose and human pose and shape parameters ($\theta$ and $\beta$) of SMPL model, and propose a two-stage learning procedure.
NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Human Pose Estimation in Top-View Fisheye Images
Through this pipeline, we create a novel dataset NToP570K (NeRF-powered Top-view human Pose dataset for fisheye cameras with over 570 thousand images), and conduct an extensive evaluation of its efficacy in enhancing neural networks for 2D and 3D top-view human pose estimation.
Occlusion Resilient 3D Human Pose Estimation
Occlusions remain one of the key challenges in 3D body pose estimation from single-camera video sequences.
Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation
We observe that previous optimization-based methods commonly rely on projection constraint, which only ensures alignment in 2D space, potentially leading to the overfitting problem.
Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras
In order to evaluate our proposed pipeline, we collect three video sets of RGBD videos recorded from multiple sparse-view depth cameras and ground truth 3D poses are manually annotated.
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.
