Pose Estimation

Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation. A common benchmark for this task is MPII Human Pose ( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Mask R-CNN

Content

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

OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields

Convolutional Pose Machines

Stacked Hourglass Networks for Human Pose Estimation

Deep High-Resolution Representation Learning for Visual Recognition

Deep High-Resolution Representation Learning for Human Pose Estimation

High-Resolution Representations for Labeling Pixels and Regions

Non-local Neural Networks

Simple Baselines for Human Pose Estimation and Tracking