2D Human Pose Estimation

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

Libraries

Datasets

Subtasks

Most implemented papers

Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

Content

Deep High-Resolution Representation Learning for Human Pose Estimation

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

Simple Baselines for Human Pose Estimation and Tracking

RMPE: Regional Multi-person Pose Estimation

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

BlazePose: On-device Real-time Body Pose tracking

Pose2Seg: Detection Free Human Instance Segmentation

Associative Embedding: End-to-End Learning for Joint Detection and Grouping