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Collision-aware interactive simulation using graph neural networks
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作者 Xin Zhu Yinling Qian +2 位作者 Qiong Wang Ziliang Feng Pheng‑Ann Heng 《Visual Computing for Industry,Biomedicine,and Art》 EI 2022年第1期174-186,共13页
Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac... Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac-tive physical simulation framework that can effectively address tool-object collisions.The framework can predict the dynamic information by considering the collision state.In particular,the graph neural network is chosen as the base model,and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests.Additionally,a novel self-supervised collision term is introduced to provide a more compact collision response.This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency. 展开更多
关键词 Deep physical simulation collision-aware Continuous collision detection Graph neural network
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