https://github.com/cschell/versatile-xr-user-identification

Implementation of "Versatile User Identification in Extended Reality Using Pretrained Similarity-Learning". Deep metric learning approach for identifying VR users from their movements.
https://github.com/cschell/versatile-xr-user-identification

Last synced: 5 months ago
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Implementation of "Versatile User Identification in Extended Reality Using Pretrained Similarity-Learning". Deep metric learning approach for identifying VR users from their movements.

• Host: GitHub
• URL: https://github.com/cschell/versatile-xr-user-identification
• Owner: cschell
• License: other
• Created: 2024-12-28T12:08:41.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-28T16:10:33.000Z (over 1 year ago)
• Last Synced: 2025-09-05T15:11:38.835Z (9 months ago)
• Language: Python
• Size: 149 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md
  • License: License.md
  • Citation: Citation.cff

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README

          
# Versatile User Identification in Extended Reality Using Pretrained Similarity-Learning

This repository contains the code for our paper ["Versatile User Identification in Extended Reality Using Pretrained Similarity-Learning"](https://arxiv.org/abs/2302.07517).

## About

This work combines distance-based and classification-based approaches to identify VR users from their movements using deep metric learning. The models are trained on data from players of "Half-Life: Alyx" and demonstrate:

- Ability to identify new users from non-specific movements with minimal enrollment data

- Fast new user enrollment (seconds vs days for retraining traditional classifiers) 

- More reliable performance with limited enrollment data

- Cross-dataset generalization to different VR devices

## Repository Structure

The codebase is organized into `data_preparation` and `machine_learning`. You find in each folder the corresponding Readmes.

## Citation

If you use this code in your research, please cite:

```bibtex

@online{RackVersatileUserIdentification2023,

  title = {Versatile {{User Identification}} in {{Extended Reality}} Using {{Pretrained Similarity-Learning}}},

  author = {Rack, Christian and Kobs, Konstantin and Fernando, Tamara and Hotho, Andreas and Latoschik, Marc Erich},

  date = {2023-07-03},

  eprint = {2302.07517},

  eprinttype = {arXiv},

  doi = {10.48550/arXiv.2302.07517}

}

```

## License

This work by Christian Rack, Konstantin Kob, Tamara Fernando, Andreas Hotho and Marc E. Latoschik is licensed under CC BY-NC-SA 4.0.