论文标题
引擎盖：用于服装动力的广义建模的分层图
HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics
论文作者
论文摘要
我们提出了一种利用图形神经网络，多层次消息传递以及无监督训练的方法，以实现对现实服装动态的实时预测。基于线性混合皮肤的现有方法必须接受特定服装的培训，但我们的方法不可知，不适合身体形状，并且适用于紧身的服装以及宽松的，自由流动的衣服。我们的方法还处理拓扑（例如带有按钮或拉链的服装）和推理时材料特性的变化。作为一项关键贡献，我们提出了一个层次结构消息方案，该方案有效地传播了僵硬的拉伸模式，同时保留了本地细节。我们从经验上表明，我们的方法在定量上优于强碱，并且其结果被认为比最新方法更现实。
We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Our method furthermore handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that our method outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.
