arXiv Computer Vision
Explicit Frictional Contact Modeling in Neural Garment Simulation
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Explicit Frictional Contact Modeling in Neural Garment Simulation
Graph neural networks (GNNs) simulate garments efficiently through physics-based self-supervision, but leave body-garment friction implicit or inside training energies, with no friction coefficient $μ...
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HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning
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TRACE: Spatiotemporal Contact Memory Graph Network Simulator for Granular Dynamics
TRACE is a graph‑network simulator that stores granular contact history on edges using attention‑based message passing and a gated recurrent unit, with an edge‑identity dictionary to preserve memory as contacts change. It predicts normal and tangential forces while enforcing Coulomb friction and internal force balance, and is trained via single‑step pretraining followed by autoregressive fine‑tuning. On 2D and 3D granular column‑collapse benchmarks, TRACE achieves significantly lower long‑rollout position and deposit errors than existing simulators, uses fewer parameters, maintains near‑zero particle interpenetration, and outperforms the material point method by 12.2× (2D) and 8.9× (3D).