Extended Short- and Long-Range Mesh Learning for Fast and Generalized Garment Simulation
This work addresses a domain-specific problem for computer graphics and simulation, offering incremental improvements in efficiency for garment modeling.
The paper tackles the computational inefficiency of graph neural networks (GNNs) in high-resolution 3D garment simulation by introducing a novel GNN-based framework with Laplacian-Smoothed Dual Message-Passing and Geodesic Self-Attention modules, achieving state-of-the-art performance with fewer layers and lower inference latency.
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.