论文标题
使用骨驱动运动网络预测宽松的服装变形
Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks
论文作者
论文摘要
我们提出了一种学习算法，该算法使用骨驱动的运动网络以交互速率预测松散的服装网格的变形。给定服装，我们使用皮肤分解从模拟的网格序列中生成模拟数据库，并从模拟的网格序列中提取虚拟骨骼。在运行时，我们分别以顺序的方式分别计算低频和高频变形。低频变形是通过将身体运动转移到虚拟骨骼的运动来预测的，并且估计高频变形可以利用虚拟骨骼动作的全局信息以及从低频网格中提取的局部信息。此外，我们的方法可以使用RBF内核结合训练的网络来估计由模拟参数（例如，织物的弯曲刚度）引起的服装变形，用于不同的模拟参数。通过广泛的比较，我们表明我们的方法在网格变形的预测准确性方面优于最先进的方法，而RMSE的预测准确性约为20％，而Hausdorff距离的预测准确性约为10％。代码和数据可在https://github.com/non-void/virtualbones上找到。
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.
