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Home > All Category Datasets > Computer Vision Datasets > 18,860 Images – 3D Human Pose & Landmarks Dataset

18,860 Images – 3D Human Pose & Landmarks Dataset

human body landmarks dataset

3D human pose dataset

human instance segmentation

annotated human body dataset

human pose estimation data

human action recognition dataset

22 landmarks human dataset

This dataset diversity includes multiple scenes, light conditions, ages, shooting angles, and poses. In terms of annotation, we adpoted instance segmentation annotations on human body. 22 landmarks were also annotated for each human body. This dataset is suitable for training and benchmarking AI models in human body analysis, gesture recognition, and multi-person detection.

This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.

Specifications

Specifications

Data size

466 people, the number of images is 18,860

Race distribution

Asian

Gender distribution

233 males, 233 females

Age distribution

299 children and teenagers, 167 adults

Collecting environment

including indoor scenes and outdoor scenes

Data diversity

multiple scenes, light conditions, ages, shooting angles, and poses

Device

RealSense Depth Camera D435i

Data format

the image data format is .jpg and .png, the annotation file format are .json，the file format of camera parameters is .txt

Collecting content

3D images of various human body poses ((the rgb channel and depth channel have been registered))

Annotation content

instance segmentation annotation of human body , 22 landmarks annotation of human body

Accuracy

Accuracy requirement: the mask edge location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; Accuracy requirement of segmentation annotation: the annotation part (each part of mask) is regarded as the unit, the accuracy rate shall be more than 95%; Accuracy requirement of landmark annotation: the annotation part (each landmark) is regarded as the unit, the accuracy rate shall be more than 95%; Annotating accuracy = number of correct annotations / total number of annotations

Sample

Sample

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