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Automation of 3D average human body shape modeling using Rhino and Grasshopper Algorithm
- Source :
- Fashion and Textiles, Vol 8, Iss 1, Pp 1-20 (2021)
- Publication Year :
- 2021
- Publisher :
- SpringerOpen, 2021.
-
Abstract
- Abstract The aim of this study is to develop an automated process for modeling average 3D human body according to body types using both NUBRS-based modeling software Rhinoceros 3D® (Rhino) and Grasshopper as an algorithm editor. First, we categorized men aged 36 to 55 years included in SizeUSA 3D data into the three body types (normal, overweight, and obese), and selected seven samples in each body type. To execute the automated process of generating an average 3D model of their lower bodies in a step-by-step manner, the following procedures were performed: (1) Determine the main reference lines on the 3D-scanned lower bodies, including six horizontal reference lines and six vertical reference lines; (2) Create horizontal and vertical line grids and intersection points (3) Generate an average 3D model in a position that corresponds to the average coordinates of the intersection points (vertex coordinates) of seven samples for each body type. A Grasshopper algorithm was formulated to automatically execute all procedures that had to be repeatedly performed. As a way to verify the average model’s size and shape, the girth measurements of the samples for each body type were averaged, and the results were compared with those of the 3D average body shape. It was found that the deviation was less than 1 cm, which indicates the validity of the 3D modeling approach applied in the present study. Each process was incorporated into commands available in the Rhino interface, and this automation allowed a number of 3D body shape modeling operations to be implemented in a significantly reduced time period.
Details
- Language :
- English
- ISSN :
- 21980802
- Volume :
- 8
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Fashion and Textiles
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7760676f84ea421d829368372aabe904
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s40691-021-00249-6