Open Collections

Shastry, Megha

University of British Columbia

Fit and sizing of clothing are fundamental problems in the field of garment design, manufacture and retail. Here we propose new computational methods for adjusting the fit of clothing on realistic models of the human body by interactively modifying desired fit attributes. Clothing fit represents the relationship between the body and the garment, and is quantified using fit attributes such as ease and pressure on the body. Such attributes are computed by physically based simulations. We propose a method to learn the relationship between the fit attributes and the space of pattern edits. In contrast to the earlier approaches that use in-the-loop physics simulation, we begin by creating a custom data set capturing all possible edits to the 2D pattern and the corresponding per-vertex fit attributes generated from their 3D drape simulations. With this data we train a model to isolate and predict changes to the 2D pattern caused by edits to the fit attributes. We provide interactive tools to directly edit the fit attributes in 3D and instantaneously predict the corresponding pattern adjustments. Our method introduces a different way to express fit adjustment that it is more intuitive and is capable of cutting short the trial-and-error period.

Text

2023-04-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81431

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/