# Cloth in the Wind: A Case Study of Physical Measurement through Simulation

arXiv: https://arxiv.org/abs/2003.05065 · HTML: https://mlsanity.com/paper/2003.05065/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On](https://mlsanity.com/paper/2105.06462/) (2021)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [Garment Avatars: Realistic Cloth Driving using Pattern Registration](https://mlsanity.com/paper/2206.03373/) (2022)
- [How Will It Drape Like? Capturing Fabric Mechanics from Depth Images](https://mlsanity.com/paper/2304.06704/) (2023)
- [DiffWind: Physics-Informed Differentiable Modeling of Wind-Driven Object Dynamics](https://mlsanity.com/paper/2603.09668/) (2026)
- [Neural inverse procedural modeling of knitting yarns from images](https://mlsanity.com/paper/2303.00154/) (2023)
- [Bayesian Differentiable Physics for Cloth Digitalization](https://mlsanity.com/paper/2402.17664/) (2024)
- [Learning Physics Properties of Fabrics and Garments with a Physics Similarity Neural Network](https://mlsanity.com/paper/2112.10727/) (2021)
- [Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussians](https://mlsanity.com/paper/2512.01306/) (2025)

## Abstract (arXiv)

For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior. Nevertheless, measuring physical properties from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces. In this paper, we propose to measure latent physical properties for cloth in the wind without ever having seen a real example before. Our solution is an iterative refinement procedure with simulation at its core. The algorithm gradually updates the physical model parameters by running a simulation of the observed phenomenon and comparing the current simulation to a real-world observation. The correspondence is measured using an embedding function that maps physically similar examples to nearby points. We consider a case study of cloth in the wind, with curling flags as our leading example -- a seemingly simple phenomena but physically highly involved. Based on the physics of cloth and its visual manifestation, we propose an instantiation of the embedding function. For this mapping, modeled as a deep network, we introduce a spectral layer that decomposes a video volume into its temporal spectral power and corresponding frequencies. Our experiments demonstrate that the proposed method compares favorably to prior work on the task of measuring cloth material properties and external wind force from a real-world video.