3D Human Pose Estimation
312 papers with code • 25 benchmarks • 47 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
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.
DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE.
PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation
Existing skeleton-based 3D human pose estimation methods only predict joint positions.
Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling
Most of the previous 3D human pose estimation work relied on the powerful memory capability of the network to obtain suitable 2D-3D mappings from the training data.
Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving
Accurate 3D human pose estimation (3D HPE) is crucial for enabling autonomous vehicles (AVs) to make informed decisions and respond proactively in critical road scenarios.
ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting
Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence.
ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning
For more accurate and physically plausible predictions in world space, our network is designed to learn human motions from a human-centric perspective, which enables the understanding of the same motion captured with different camera trajectories.
Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view
This is because we have a mental model that allows us to imagine a person's appearance at different viewing directions from a given image and utilize the consistency between them for inference.
Representation learning of vertex heatmaps for 3D human mesh reconstruction from multi-view images
We show that representation learning of vertex heatmaps using an autoencoder helps improve the performance of such approaches.
