  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.
