We present a learning algorithm that uses bone-driven motion networks to
predict the deformation of loose-fitting garment meshes at interactive rates.
Given a garment, we generate a simulation database and extract virtual bones
from simulated mesh sequences using skin decomposition. At runtime, we
separately compute low- and high-frequency deformations in a sequential manner.
The low-frequency deformations are predicted by transferring body motions to
virtual bones' motions, and the high-frequency deformations are estimated
leveraging the global information of virtual bones' motions and local
information extracted from low-frequency meshes. In addition, our method can
estimate garment deformations caused by variations of the simulation parameters
(e.g., fabric's bending stiffness) using an RBF kernel ensembling trained
networks for different sets of simulation parameters. Through extensive
comparisons, we show that our method outperforms state-of-the-art methods in
terms of prediction accuracy of mesh deformations by about 20% in RMSE and 10%
in Hausdorff distance and STED. The code and data are available at
this https URL

该研究通过骨骼运动网络来预测宽松服装网格的变形，包括低频和高频的变形，并可估算仿真参数变化引起的衣服变形，证明了该方法优于现有方法。