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.
Libraries
Use these libraries to find 2D Human Pose Estimation models and implementationsDatasets
Most implemented papers
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
In this paper, we show the surprisingly good capabilities of plain vision transformers for pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model called ViTPose.
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Human pose estimation has made significant progress during the last years.
Preconditioned Stochastic Gradient Descent
When stochastic gradient is used, it can naturally damp the gradient noise to stabilize SGD.
Learning from Synthetic Humans
In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
Whole-Body Human Pose Estimation in the Wild
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults with Dementia
This work leverages novel spatial-temporal graph convolutional network (ST-GCN) architectures and training procedures to predict clinical scores of parkinsonism in gait from video of individuals with dementia.
Event Neural Networks
Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated.