28 datasets found

The dataset used in the paper is the LEMO dataset.

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance...

Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance...

InstaVariety dataset is used for training the model, and it contains images of people performing various actions.

VLOG-People dataset is used for training the model, and it contains videos of people performing various actions.

Human pose estimation refers to the task of recognizing postures by localizing body keypoints (head, shoulders, elbows, wrists, knees, ankles, etc.) from images.

Human pose estimation dataset

MuPoTS-3D is a representative dataset for monocular 3DMPPE, composed of 20 few-second-long sequences with 2–3 people interacting with each other.

Convolutional pose machines

Deepercut: A deeper, stronger, and faster multi-person pose estimation model

The MPII Human Pose dataset contains around 25K images with full-body pose annotations.

The MPII keypoint dataset contains 25,000 images with 40,000 human instances labeled with 16 keypoints.

The COCO keypoint-2017 dataset contains over 200,000 images and 250,000 human instances labeled with 17 keypoints.

Human pose estimation is one fundamental yet challenging task to estimate precise human joint coordinates (eyes, ears, shoulders, elbows, wrists, knees, etc.).

A Simple Yet Effective Baseline for 3D Human Pose Estimation.

Human pose estimation dataset

Human pose estimation dataset

Human pose estimation using Spatial Attention-based Distribution Integration Network (SADI-NET)

The COCO keypoint benchmark is a widely used dataset for human pose estimation.