# CloSE: A Geometric Shape-Agnostic Cloth State Representation

arXiv: https://arxiv.org/abs/2504.05033 · HTML: https://mlsanity.com/paper/2504.05033/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 80 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://mlsanity.com/paper/2103.11511/) (2021)
- [Investigating Bias and Fairness in Facial Expression Recognition](https://mlsanity.com/paper/2007.10075/) (2020)
- [MovieGraphs: Towards Understanding Human-Centric Situations from Videos](https://mlsanity.com/paper/1712.06761/) (2017)
- [Deep Reasoning with Knowledge Graph for Social Relationship Understanding](https://mlsanity.com/paper/1807.00504/) (2018)
- [Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset](https://mlsanity.com/paper/2012.14259/) (2020)
- [Learning Interactions and Relationships between Movie Characters](https://mlsanity.com/paper/2003.13158/) (2020)
- [Dual-Glance Model for Deciphering Social Relationships](https://mlsanity.com/paper/1708.00634/) (2017)
- [Graph-Based Social Relation Reasoning](https://mlsanity.com/paper/2007.07453/) (2020)
- [SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization](https://mlsanity.com/paper/2410.21411/) (2024)
- [An Open-source Benchmark of Deep Learning Models for Audio-visual Apparent and Self-reported Personality Recognition](https://mlsanity.com/paper/2210.09138/) (2022)
- [An End-to-End Network for Generating Social Relationship Graphs](https://mlsanity.com/paper/1903.09784/) (2019)
- [Multi-Granularity Reasoning for Social Relation Recognition from Images](https://mlsanity.com/paper/1901.03067/) (2019)
- [Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition](https://mlsanity.com/paper/2208.06174/) (2022)

## Abstract (arXiv)

Cloth manipulation is a difficult problem mainly because of the non-rigid nature of cloth, which makes a good representation of deformation essential. We present a new representation for the deformation-state of clothes. First, we propose the dGLI disk representation based on topological indices computed for edge segments of the cloth border that are arranged on a circular grid. The heat-map of the dGLI disk uncovers patterns that correspond to features of the cloth state that are consistent for different shapes, sizes or orientation of the cloth. We then abstract these important features from the dGLI disk into a circle, calling it the Cloth StatE representation (CloSE). This representation is compact, continuous, and general for different shapes. We show that this representation is able to accurately predict the fold locations for several simulation clothing datasets. Finally, we also show the strengths of this representation in two relevant applications: semantic labeling and high- and low-level planning. The code and the dataset can be accessed from: https://close-representation.github.io/