A reduced-order model represents woven weaver interactions via nodes and four stiffness elements (axial, uncrimping, shear, frictional slip), calibrated to 5% agreement on bending and shear data, then used to demonstrate emergent Poisson response, pullout, tearing, and anisotropy.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
MuNet is an end-to-end graph convolutional network using 2-manifold graphs and a mutualistic training mechanism that jointly optimizes 3D human mesh recovery and clothed reconstruction, reporting state-of-the-art results on six benchmarks.
citing papers explorer
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A Reduced Order Model for Emergent Mechanics in Woven Systems
A reduced-order model represents woven weaver interactions via nodes and four stiffness elements (axial, uncrimping, shear, frictional slip), calibrated to 5% agreement on bending and shear data, then used to demonstrate emergent Poisson response, pullout, tearing, and anisotropy.
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MuNet: A Mutualistic Network for Joint 3D Human Mesh Recovery and 3D Clothed Human Reconstruction from Single Images
MuNet is an end-to-end graph convolutional network using 2-manifold graphs and a mutualistic training mechanism that jointly optimizes 3D human mesh recovery and clothed reconstruction, reporting state-of-the-art results on six benchmarks.