Abstract
Being able to customize sewing patterns for different human bodies without using any pre-defined adjustment rule will not only improve the realism of virtual humans in the entertainment industry, but also deeply affect the fashion industry by making fast fashion and made-to-measure garments more accessible. To meet the requirement set by the fashion industry, a sewing pattern adjustment system must be both efficient and precise, which unfortunately cannot be achieved by existing techniques. In this paper, we propose to solve sewing pattern adjustment as a nonlinear optimization problem immediately, rather than in two phases: a garment shape optimization phase and an inverse pattern design phase as in previous systems. This allows us to directly minimize the objective function that evaluates the fitting quality of the garment sewn from a pattern, without any compromise caused by the nonexistence of the solution to inverse pattern design. To improve the efficiency of our system, we carry out systematic research on a variety of optimization topics, including pattern parametrization, initialization, an inexact strategy, acceleration, and CPU-GPU implementation. We verify the usability of our system through automatic grading tests and made-to-measure tests. Designers and pattern makers confirm that our pattern results are able to preserve design details and their fitting qualities are acceptable. In our computational experiment, the system further demonstrates its efficiency, reliability, and flexibility of handling various pattern designs. While our current system still needs to overcome certain limitations, we believe it is a crucial step toward fully automatic pattern design and adjustment in the future.
Huamin Wang
Chief Scientist
My research interests include physics-based simulation and modeling, generative AI, numerical analysis and nonlinear optimization.