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English(EN) FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes

FreeForm方法实现更快、更准确的可变形物体模拟

研究人员开发了一种名为FreeForm的新方法,用于模拟可变形超弹性物体,而无需依赖传统的网格。该方法利用再生核粒子法(RKPM)通过求解弹性能量Hessian矩阵上的特征系统来创建降阶蒙皮权重。与现有的神经场技术相比,该方法在训练速度和模拟误差方面都有显著提升,并已在机器人模拟应用中得到验证。 AI

排序理由 详细介绍新模拟方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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FreeForm方法实现更快、更准确的可变形物体模拟

报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Donglai Xiang, Vismay Modi, Rishit Dagli, Ty Trusty, Gilles Daviet, Anka He Chen, Nicholas Sharp, David I. W. Levin ·

    FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes

    arXiv:2605.29318v1 Announce Type: cross Abstract: We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynamic simulation represents the input geometry by either meshes, which can be diffic…