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Computer Science > Graphics

arXiv:2608.17833 (cs)
[Submitted on 18 Aug 2026]

Title:Variational r-Adaptive Cloth Simulation

Authors:Jiahao Wen, Zhen Chen, Jernej Barbič, Danny M. Kaufman
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Abstract:We present the first r-adaptive method for simulating cloth dynamics and statics with frictional contact in modern cloth pipelines. Thin cloth requires high effective spatial resolution to reproduce wrinkles, folds, buckling, and sharp contact features. However, applying existing variational r-adaptivity to piecewise-linear shells reveals two coupled failure modes. Discretized incremental-potential (IP) optimization can become trapped in poor local minima, yielding suboptimal physical configurations. It can also lower IP artificially by collapsing elements, invalidating the finite-element approximation on which the objective relies. We address both problems with degeneracy-activated quality regularization. The regularizer remains inactive for well-shaped elements, preserving anisotropic adaptation and local densification, but becomes strong near degeneracy. It suppresses spurious low-energy basins, improves escape from suboptimal physical minima, and prevents element bunching, a cloth-specific failure in which elements progressively collapse as cloth slides across sharp contact features. For practical performance, we introduce a dynamic nonlinear solver that exploits within-timestep coherence through accelerated derivative evaluation and dynamic IPC tolerance updates for r-adaptive iterative trust-region (ITR) solves. This yields a 3-6x speedup over prior optimal ITR. Experiments on challenging frictional-contact scenarios show that, under equal vertex-count and time-budget constraints, our method achieves higher visual fidelity than fixed meshes.
Comments: 11 pages
Subjects: Graphics (cs.GR)
Cite as: arXiv:2608.17833 [cs.GR]
  (or arXiv:2608.17833v1 [cs.GR] for this version)
  https://doi.org/10.48550/arXiv.2608.17833
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jiahao Wen [view email]
[v1] Tue, 18 Aug 2026 14:34:31 UTC (2,780 KB)
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