用双模块设计提升3D衣物模拟速度与精度。
Extended Short- and Long-Range Mesh Learning for Fast and Generalized Garment Simulation
- 引入拉普拉斯平滑与测地自注意力并行处理短距与长距信息。
- 仅需更少网络层，推理延迟降低，性能超越现有方法。
- 适合需要快速高保真衣物模拟的影视与游戏开发场景。
3D衣物模拟是生成布料相关图形的关键环节。近年来，图神经网络（GNN）为高效衣物模拟提供了新思路。然而，传统GNN需大量消息传递以传播物理力并维持全网格接触感知，导致高分辨率下计算效率下降。为此，本文提出一种新型GNN基网格学习框架，包含两个核心组件：拉普拉斯平滑双消息传递（LSDMP）和测地自注意力（GSA）。LSDMP通过拉普拉斯特征平滑，高效传播顶点影响至邻近区域；GSA则引入测地距离嵌入，利用注意力机制捕捉全局网格信息。两者并行运行，兼顾短程与远程建模。大量实验表明，该方法在更少层数下实现最优性能，推理延迟更低。
原文摘要 · Abstract (English)
3D garment simulation is a critical component for producing cloth-based graphics. Recent advancements in graph neural networks (GNNs) offer a promising approach for efficient garment simulation. However, GNNs require extensive message-passing to propagate information such as physical forces and maintain contact awareness across the entire garment mesh, which becomes computationally inefficient at higher resolutions. To address this, we devise a novel GNN-based mesh learning framework with two key components to extend the message-passing range with minimal overhead, namely the Laplacian-Smoothed Dual Message-Passing (LSDMP) and the Geodesic Self-Attention (GSA) modules. LSDMP enhances message-passing with a Laplacian features smoothing process, which efficiently propagates the impact of each vertex to nearby vertices. Concurrently, GSA introduces geodesic distance embeddings to represent the spatial relationship between vertices and utilises attention mechanisms to capture global mesh information. The two modules operate in parallel to ensure both short- and long-range mesh modelling. Extensive experiments demonstrate the state-of-the-art performance of our method, requiring fewer layers and lower inference latency.
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