2D Human Pose Estimation
63 papers with code • 5 benchmarks • 22 datasets
What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
Benchmarks
These leaderboards are used to track progress in 2D Human Pose Estimation
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Libraries
Use these libraries to find 2D Human Pose Estimation models and implementationsDatasets
JHMDB
AGORA
OCHuman
UAV-Human
COCO-WholeBody
Alibaba Cluster Trace
DeepSport Dataset
Latest papers with no code
Transformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition
The model uses 2D-pose features as the positional embedding of the transformer architecture and spatio-temporal features as the main input to the encoder of the transformer.
On the Calibration of Human Pose Estimation
Most 2D human pose estimation frameworks estimate keypoint confidence in an ad-hoc manner, using heuristics such as the maximum value of heatmaps.
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.
Prior-guided Source-free Domain Adaptation for Human Pose Estimation
To address this limitation, we focus on the task of source-free domain adaptation for pose estimation, where a source model must adapt to a new target domain using only unlabeled target data.
Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation
One of the mainstream schemes for 2D human pose estimation (HPE) is learning keypoints heatmaps by a neural network.
Efficient Vision Transformer for Human Pose Estimation via Patch Selection
While Convolutional Neural Networks (CNNs) have been widely successful in 2D human pose estimation, Vision Transformers (ViTs) have emerged as a promising alternative to CNNs, boosting state-of-the-art performance.
Human Pose Estimation in Monocular Omnidirectional Top-View Images
In our work we propose a new dataset for training and evaluation of CNNs for the task of keypoint detection in omnidirectional images.
Distilling Token-Pruned Pose Transformer for 2D Human Pose Estimation
Our method leverages the output of a pre-trained TokenPose to supervise the learning process of PPT.
Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History
With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators.
Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach
Human pose estimation has been widely applied in various industries.
