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

arXiv: https://arxiv.org/abs/2302.07517 · HTML: https://mlsanity.com/paper/2302.07517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 67 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [NormFace: L2 Hypersphere Embedding for Face Verification](https://mlsanity.com/paper/1704.06369/) (2017)
- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR](https://mlsanity.com/paper/2208.05604/) (2022)

## Built on by (most cited first, 2 of 2)

- [Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning](https://mlsanity.com/paper/2509.08539/) (2025)
- [Unobtrusive In-Situ Measurement of Behavior Change by Deep Metric Similarity Learning of Motion Patterns](https://mlsanity.com/paper/2509.04174/) (2025)

## Abstract (arXiv)

Various machine learning approaches have proven to be useful for user verification and identification based on motion data in eXtended Reality (XR). However, their real-world application still faces significant challenges concerning versatility, i.e., in terms of extensibility and generalization capability. This article presents a solution that is both extensible to new users without expensive retraining, and that generalizes well across different sessions, devices, and user tasks. To this end, we developed a similarity-learning model and pretrained it on the "Who Is Alyx?" dataset. This dataset features a wide array of tasks and hence motions from users playing the VR game "Half-Life: Alyx". In contrast to previous works, we used a dedicated set of users for model validation and final evaluation. Furthermore, we extended this evaluation using an independent dataset that features completely different users, tasks, and three different XR devices. In comparison with a traditional classification-learning baseline, our model shows superior performance, especially in scenarios with limited enrollment data. The pretraining process allows immediate deployment in a diverse range of XR applications while maintaining high versatility. Looking ahead, our approach paves the way for easy integration of pretrained motion-based identification models in production XR systems.