Shape Prediction of Knitted Stitches Using Machine Learning Toward Wearing Simulation of Knitted Clothes

抄録

Recently, many wearing simulation systems, which do not need samples of clothes or actual trying on them, have been developed to improve the productivity of clothes. However, in the case of knitted clothes, conventional systems only offer a looking not based on mechanical consideration at the stitch level because such consideration leads to a significant increase in computational cost. In this paper, we propose a shape prediction method for knitted stitches using machine learning. First, a yarn is modeled as a structure with straight springs, rotating springs, and torsion springs. By minimizing the potential energy of yarns, the stable shape of a stitch can be derived. Next, using such shapes as training data, machine learning was performed with nonlinear neural networks. Then, various shapes of the stitch can be predicted without time-consuming optimization. Our proposed method will be useful for precise wearing simulation of knitted clothes.