Physics-driven Pattern Adjustment for Direct 3D Garment Editing
Aric Bartle1 Alla Sheffer2 Vladimir G. Kim3 Danny M. Kaufman3 Nicholas Vining2 Floraine Berthouzoz31Stanford University 2University of British Columbia 3Adobe Research
Abstract
Designers frequently reuse existing designs as a starting point for
creating new garments. In order to apply garment modifications,
which the designer envisions in 3D, existing tools require meticulous
manual editing of 2D patterns. These 2D edits need to account
both for the envisioned geometric changes in the 3D shape, as well
as for various physical factors that affect the look of the draped
garment. We propose a new framework that allows designers to
directly apply the changes they envision in 3D space; and creates
the 2D patterns that replicate this envisioned target geometry when
lifted into 3D via a physical draping simulation. Our framework removes
the need for laborious and knowledge-intensive manual 2D
edits and allows users to effortlessly mix existing garment designs
as well as adjust for garment length and fit. Following each user
specified editing operation we first compute a target 3D garment
shape, one that maximally preserves the input garment’s style–its
proportions, fit and shape–subject to the modifications specified by
the user. We then automatically compute 2D patterns that recreate
the target garment shape when draped around the input mannequin
within a user-selected simulation environment. To generate
these patterns, we propose a fixed-point optimization scheme that
compensates for the deformation due to the physical forces affecting
the drape and is independent of the underlying simulation tool
used. Our experiments show that this method quickly and reliably
converges to patterns that, under simulation, form the desired target
look, and works well with different black-box physical simulators.
We demonstrate a range of edited and resimulated garments, and
further validate our approach via expert and amateur critique, and
comparisons to alternative solutions.
Paper
ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Video
Siggraph 2016 Demo Video
