We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network. The characters processed in our approach are not limited to humans, and can be other skeletal-based representations of non-human targets such as fish or pets. We use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse and wrinkle features as the overall residual from the template cloth mesh. Our network is used to predict the deformation for loose or tight-fitting clothing or dresses. We ensure that the memory footprint of our network is low, and thereby result in reduced storage and computational requirements. In practice, our prediction for a single cloth mesh for the skeleton-based character takes about 7 milliseconds on an NVIDIA GeForce RTX 3090 GPU. Compared with prior methods, our network can generate fine deformation results with details and wrinkles.

翻译：我们提出了一种新颖的学习方法，通过双流网络预测骨骼角色的布料变形。该方法处理的角色不仅限于人类，还可适用于鱼类或宠物等其他基于骨骼表示的非人类目标。我们采用由骨骼残差网络与网格残差网络构成的新型网络架构，从模板布料网格中学习粗粒度形变特征与褶皱特征作为整体残差。该网络可预测宽松或紧身服装及裙装的形变。我们确保网络的内存占用率较低，从而降低存储与计算需求。实际应用中，在NVIDIA GeForce RTX 3090 GPU上，预测单个骨骼角色布料网格仅需约7毫秒。与现有方法相比，我们的网络能够生成包含细节与褶皱的精细形变结果。