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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
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
Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training
hnuzhy/multiaugs
•
•
The 2D human pose estimation (HPE) is a basic visual problem.
Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning
princetonvisualai/hpe-inductive-prior-tuning
•
•
We (1) analyze the relationship between reconstruction quality and pose estimation accuracy, (2) develop a model pipeline that outperforms the baseline which inspired our work, using less than one-third the amount of training data, and (3) offer a new metric suitable for self-supervised settings that measures the consistency of predicted body part length proportions.
UniPose: Detecting Any Keypoints
This work proposes a unified framework called UniPose to detect keypoints of any articulated (e. g., human and animal), rigid, and soft objects via visual or textual prompts for fine-grained vision understanding and manipulation.
Effective Whole-body Pose Estimation with Two-stages Distillation
Different from the previous self-knowledge distillation, this stage finetunes the student's head with only 20% training time as a plug-and-play training strategy.
Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
aleflabo/MoCoDAD
•
•
ICCV 2023
Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset'ness of anomalies.
Improving 2D Human Pose Estimation across Unseen Camera Views with Synthetic Data
Human Pose Estimation is a thoroughly researched problem; however, most datasets focus on the side and front-view scenarios.
Data-Free Backbone Fine-Tuning for Pruned Neural Networks
holzbock/dfbf
•
•
In particular, the pruned network backbone is trained with synthetically generated images, and our proposed intermediate supervision to mimic the unpruned backbone's output feature map.
Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registration
Recovering the actual 3D movement of the limbs (kinematics) of the athletes requires lifting these 2D pixel locations back into a third dimension, implying a certain scene geometry.
RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose
Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency.
Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module
The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models.
