布料模拟中可学习的持久褶皱形成

中文摘要 AI 辅助

织物的机械记忆常导致持久褶皱，这些褶皱反映了关键的物理属性和习惯性穿着模式。准确模拟这些褶皱对于数字服装的视觉真实性至关重要，然而由于缺乏精确的数据集和估计方法，目前尚无专门的方法来推断控制其形成的参数。我们引入了Fabric-101，一个全面、准确且可扩展的织物数据集，包含超过101种符合纺织标准的常见织物。与现有数据集不同，它通过循环加载-卸载测量捕捉了三种物理上不同的变形分量（即自恢复（弹性）、可恢复（摩擦驱动）和不可恢复（塑性））。基于这些数据，我们提出了一种可微分的布料模拟器，结合了弹塑性模型与摩擦，旨在捕捉可恢复和不可恢复的褶皱形成。我们的模拟器是可微分的，并使用伴随方法从测量的滞后曲线中学习织物物理参数，从而学习特定织物的褶皱行为。通过大量实验，我们证明了我们的模型能够再现与真实织物在视觉和物理上相似的持久褶皱，涵盖多种材料和运动。数据集和代码可在以下网址获取：https URL。

英文摘要

The mechanical memory of fabrics often leads to persistent wrinkles, which reflect key physical properties and habitual wear patterns. Simulating these wrinkles accurately is essential for visual plausibility in digital garments, yet no dedicated approach exists for inferring the parameters that govern their formation due to the lack of precise datasets and estimation methods. We introduce Fabric-101, an inclusive, accurate, and extendable fabric dataset comprising over 101 common fabrics following textile standards. Unlike existing datasets, it captures three physically distinct deformation components (i.e., self-recoverable (elastic), recoverable (friction-driven), and unrecoverable (plastic)), from cyclic loading-unloading measurements. Building on this data, we propose a differentiable cloth simulator combining an elasto-plastic model with friction, designed to capture recoverable and unrecoverable wrinkle formation. Our simulator is differentiable and uses adjoint method to learn fabric physical parameters from the measured hysteresis curves, learning fabric-specific wrinkle behaviors. Through extensive experiments, we demonstrate that our model reproduces persistent wrinkles that are visually and physically similar to real fabrics across diverse materials and motions. Dataset and code are available in https://github.com/GongDeshan/Fabric_101_for_Wrinkles.

发表机构

• The University of Hong Kong（香港大学）
• University of Leeds（利兹大学）
• University College London（伦敦大学学院）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

A paper accepted in the SIGRRAPH Asia 2026 conference