Automatic Estimation of Anthropometric Human Body Measurements

TL;DR

This paper explores deep learning methods for estimating human body measurements from visual data, addressing data scarcity by creating a synthetic dataset with ground truth annotations to improve accuracy and applicability.

Contribution

It introduces a novel approach combining deep learning with synthetic data generation for accurate anthropometric measurement estimation from images and 3D data.

Findings

Synthetic dataset improves measurement estimation accuracy.

Deep learning models effectively predict body measurements.

Method applicable to various visual data types.

Abstract

Research tasks related to human body analysis have been drawing a lot of attention in computer vision area over the last few decades, considering its potential benefits on our day-to-day life. Anthropometry is a field defining physical measures of a human body size, form, and functional capacities. Specifically, the accurate estimation of anthropometric body measurements from visual human body data is one of the challenging problems, where the solution would ease many different areas of applications, including ergonomics, garment manufacturing, etc. This paper formulates a research in the field of deep learning and neural networks, to tackle the challenge of body measurements estimation from various types of visual input data (such as 2D images or 3D point clouds). Also, we deal with the lack of real human data annotated with ground truth body measurements required for training and evaluation, by generating a synthetic dataset of various human body shapes and performing a skeleton-driven annotation.