cs.CVOct 24, 2026

Explicit Frictional Contact Modeling in Neural Garment Simulation

Authors: Thanh-Nguyen Truong, Vu-Hoang Tran, Ching-Chun Huang

Organizations: National Yang Ming Chiao Tung University, Taiwan · Ho Chi Minh City University of Technology and Engineering, Vietnam

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Abstract

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 μμ to set at inference; retraining the tested GNNs with a coefficient-specific friction term barely responds to μμ. Optimization-based solvers decide each contact's stick-slip state from the reaction that governs it, but only by re-optimizing the garment state at every step; a friction term in a one-pass training objective acts as a fixed-weight penalty on sliding, and the trained weights apply no per-contact Coulomb condition at inference. We therefore resolve friction explicitly after prediction: a Modular Contact Operator, attached to a frozen predictor without retraining or an iterative solve, estimates the normal load, applies a closed-form Coulomb correction along a path across body triangles, and propagates the induced motion through the cloth as a linearized membrane wave. On HOOD and ContourCraft, it raises sticking and lowers slip monotonically in μμ, tracking a reference solver's response while coefficient-specific retraining stays flat, with low penetration and reference-consistent stretching at an eighth of the solver's runtime.

Figures & tables

Figure 2: Pipeline of the Modular Contact Operator. A frozen GNN maps the current autoregressive state to a tentative state; the four stages correct it in the order shown, and the corrected state re-enters the rollout history for the next prediction.
Figure 3: Geometry-Aware Contact Traversal. Left: from the same endpoints xˉ\bar{\mathbf{x}} and x^\hat{\mathbf{x}} on a folded mesh, the ambient chord (green) passes through the surface and lands x\mathbf{x} (chord) inside the body, whereas our triangle walk (blue) follows the crossed faces and keeps x\mathbf{x} (traversal) at its contact clearance. Right: unfolding those faces about their hinges develops the walk into a straight segment.
Figure 4: Friction-Induced Motion Propagation within one frame. Left to right: the tentative state with the tangential correction cT,i\mathbf{c}_{T,i} of contact ii ; the Coulomb correction alone, which over-stretches the edges to the uncorrected neighbours; propagation at 0<τ<h0<\tau<h , where the contact follows the ramp wi(τ)\mathbf{w}_{i}(\tau) and the elastic pull −K0w-\mathbf{K}_{0}\mathbf{w} carries the surrounding fabric; and τ=h\tau=h , where the neighbours have moved by w(h)\mathbf{w}(h) and no displacement discontinuity remains.
Figure 5: μ\mu -dependent frictional response over the 4040 matched sequences. Left: pooled mass–time stick fraction at the operator’s static band ϵv\epsilon_{v} (Sec. 4.3 ). Right: pooled mass–time relative slip speed, each candidate normalized by its own μ=0\mu=0 rollout. EfrictionE_{\mathrm{friction}} curves are coefficient-specific retrainings.
Figure 6: Qualitative μ\mu -dependent frictional response. Top: tank-top armhole after a dance motion at μ∈{0,0.3}\mu\in\{0,0.3\} on ContourCraft; bottom: long-sleeve cuff during an arm raise at μ∈{0.1,0.6}\mu\in\{0.1,0.6\} on HOOD. Each group shows PPF, the backbone with our operator, and coefficient-specific EfrictionE_{\mathrm{friction}} retraining, lower μ\mu at left.
Figure 7: Inference-time control of the friction coefficient. Left: a long skirt over an arabesque at μ∈{0.05,0.50}\mu\in\{0.05,0.50\} , with insets enlarging the skirt–leg contact. Right: a hooded dress at μ∈{0.20,0.40}\mu\in\{0.20,0.40\} . All rollouts use our operator on one retrained ContourCraft backbone; the garments and motions were absent from its training.
MethodMean pen. ↓\downarrowEstr(μ)/Estr(0)E_{\mathrm{str}}(\mu)/E_{\mathrm{str}}(0)
[ 10−3 %10^{-3}\,\% ]μ=0.1\mu=0.1μ=0.3\mu=0.3μ=0.6\mu=0.6
PPF (reference solver)0.21.0041.0381.314
ContourCraft ++ our operator6.10.9941.0471.139
ContourCraft ++ native EfrictionE_{\mathrm{friction}}10.40.9270.9270.924
HOOD ++ our operator8.61.0031.0401.073
HOOD ++ native EfrictionE_{\mathrm{friction}}13.91.0040.9941.029
Table 1: Garment-quality checks over the matched grid. Mean penetration: fraction of garment vertex-frames with negative signed body distance, averaged over μ∈{0.1,0.3,0.6}\mu\in\{0.1,0.3,0.6\} and the 4040 sequences; the lower mean within each backbone is bolded. Stretching-energy ratio: each candidate’s frame-mean EstretchingE_{\mathrm{stretching}} at μ\mu relative to its μ=0\mu=0 rollout, averaged over the 4040 sequences; shaded rows increase monotonically with μ\mu , as PPF’s does.
ConfigurationMean pen. ↓\downarrowDeviation to reference [%] ↓\downarrow
[ 10−3 %10^{-3}\,\% ]μ=0.1\mu=0.1μ=0.3\mu=0.3μ=0.6\mu=0.6
Coulomb correction11.52.9 (1.6)13.3 (10.5)38.6 (27.5)
++ traversal6.52.3 (1.4)12.6 (9.5)36.8 (26.5)
++ propagation9.23.0 (1.4)1149.0 (16.5)1268.6 (114.1)
++ strain limiting (ours)6.12.8 (1.1)6.9 (4.3)17.3 (11.1)
Table 2: Cumulative component ablations. Starting from normal-load estimation and Coulomb correction, subsequent rows add traversal, propagation, and strain limiting in execution order on the retrained ContourCraft backbone with checkpoint, contact set, and μ=0\mu=0 anchor shared. Mean penetration: fraction of garment vertex-frames with negative signed body distance over the 4040 sequences and μ∈{0.1,0.3,0.6}\mu\in\{0.1,0.3,0.6\} . Deviation to reference: per-sequence 100 ∣r/rref−1∣100\,|r/r_{\mathrm{ref}}-1| between the row’s and PPF’s stretching-energy ratios, mean (median). Bold: lowest mean per column.
Figure 8: Qualitative component ablation , ContourCraft backbone, μ=0.3\mu=0.3 ; columns follow the rows of Table 2 . Under the same body motion, Coulomb correction alone drives cloth through the thigh (arrow); traversal closes the crossing but leaves the fabric taut; unlimited propagation gives unstable, excessive stretching; strain limiting removes both.
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