Authors:
David Bojanić
1
;
Kristijan Bartol
2
;
Tomislav Petković
1
and
Tomislav Pribanić
1
Affiliations:
1
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb, Croatia
;
2
TU Dresden, 01062 Dresden, Dresden, Germany
Keyword(s):
Anthropometry, Body Measurements, 3D Landmarks.
Abstract:
The current state-of-the-art 3D anthropometry extraction methods are either template-based or landmark-based. Template-based methods fit a statistical human body model to a 3D scan and extract complex features from the template to learn the body measurements. The fitting process is usually an optimization process, sensitive to its hyperparameters. Landmark-based methods use body proportion heuristics to estimate the landmark locations on the body in order to derive the measurements. Length measurements are derived as distances between landmarks, whereas circumference measurements are derived as cross-sections of the body and a plane defined at the desired landmark location. This makes it very susceptible to noise in the 3D scan data. To address these issues, we propose a simple learning method that infers the body measurements directly using the landmarks defined on the body. Our method avoids fitting a body model, extracting complex features, using heuristics, and handling noise in
the data. We compare our method on the CAESAR dataset and show that using a simple method coupled with sparse landmark data can compete with state-of-the-art methods. To take a step towards open-source 3D anthropometry, we make our code available at https:/github.com/DavidBoja/Landmarks2Anthropometry.
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