Automatic Estimation of Anthropometric Human Body Measurements
This work solves the problem of accurate body measurement estimation for applications in ergonomics and garment manufacturing, but it is incremental as it builds on existing deep learning methods.
The paper tackles the challenge of estimating anthropometric body measurements from visual data like 2D images or 3D point clouds, addressing the lack of real annotated data by generating a synthetic dataset with skeleton-driven annotation.
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.