Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks

TL;DR

This paper introduces a bone-driven motion network that predicts loose-fitting garment deformations in real-time by combining low- and high-frequency deformation modeling, outperforming previous methods in accuracy.

Contribution

The novel approach integrates bone-driven motion networks with a dual-frequency deformation prediction and parameter variation estimation, enabling accurate and interactive garment deformation prediction.

Findings

Achieves 20% better RMSE in deformation prediction

Outperforms state-of-the-art in Hausdorff distance and STED

Enables deformation estimation across different simulation parameters

Abstract

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.