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 https://github.com/non-void/VirtualBones.

翻译：我们提出了一个学习算法,使用骨质驱动运动网络来预测以交互速率对松散的成衣模制衣物的变形。根据服装,我们用皮肤分解法生成模拟数据库,并从模拟网状序列中提取虚拟骨骼。在运行时,我们以顺序方式分别计算低频和高频变形。低频变形是通过将身体动作转换到虚拟骨骼动作来预测的,高频变形是利用虚拟骨骼运动和从低频间歇物中提取的地方信息的全球信息进行估计的。此外,我们的方法可以使用REBF内核(例如,织物弯曲的僵硬性)参数变化引起的服装变形利用经过训练的网络为不同的模拟参数组合进行估计。通过广泛的比较,我们显示,我们的方法在预测网形变精度变形的精确度方面,大约20%的RMSE和10 %的Hausdorf距离和STEDones。在 https://giualub./nonarve.b.