# Learning Physics Properties of Fabrics and Garments with a Physics Similarity Neural Network

arXiv: https://arxiv.org/abs/2112.10727 · HTML: https://mlsanity.com/paper/2112.10727/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects](https://mlsanity.com/paper/2011.08177/) (2020)
- [Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks](https://mlsanity.com/paper/2004.04674/) (2020)
- [Cloth in the Wind: A Case Study of Physical Measurement through Simulation](https://mlsanity.com/paper/2003.05065/) (2020)
- [3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations](https://mlsanity.com/paper/1805.00328/) (2018)

## Built on by (most cited first, 1 of 1)

- [Recognising Known Configurations of Garments For Dual-Arm Robotic Flattening](https://mlsanity.com/paper/2205.00225/) (2022)

## Abstract (arXiv)

In this paper, we propose to predict the physics parameters of real fabrics and garments by learning their physics similarities between simulated fabrics via a Physics Similarity Network (PhySNet). For this, we estimate wind speeds generated by an electric fan and the area weight to predict bending stiffness of simulated and real fabrics and garments. We found that PhySNet coupled with a Bayesian optimiser can predict physics parameters and improve the state-of-art by 34%for real fabrics and 68% for real garments.