Cross-Application XR Motion Dataset

• Cross-Application XR Motion Dataset is a curated collection of multi-session motion capture recordings from users interacting with distinct XR applications.
• It standardizes diverse data using consistent feature vectors (18 features at 30 Hz) and preprocessing techniques like BRV encoding to reduce context bias.
• Deep learning models applied to this dataset reveal high within-app accuracy but struggle with cross-application generalization, underscoring security and privacy challenges.

A Cross-Application XR Motion Dataset is a collection of motion capture data acquired from individual users as they interact with multiple, distinct Extended Reality (XR) applications—such as VR games with differing mechanics, social virtual environments, and training simulations. The central aim is to enable the paper of user motion signatures and behavioral biometrics across heterogeneous application contexts, supporting the development and benchmarking of algorithms for identification, security, and personalization in XR and metaverse platforms. Such datasets capture the variability in user behavior induced by differing tasks, environments, and device configurations, thereby providing a rigorous test bed for evaluating the generalization capacity of data-driven models.

1. Dataset Composition and Characteristics

Cross-application XR motion datasets are characterized by their inclusion of multi-session motion recordings from the same set of users performing varied tasks in multiple XR applications, often combining game-centric, unstructured, and social-interaction contexts. For example, the dataset introduced in (Schach et al., 10 Sep 2025) consists of 49 participants engaging with five XR applications, including rhythm games (Synth Riders, Beat Saber), first-person shooter scenarios (Superhot VR, Half-Life: Alyx), and a social VR platform with no fixed task constraints. Motion data is captured consistently across contexts—typically at a standard frame rate (e.g., 30 Hz) and with a uniform feature space (e.g., positions and rotations of the HMD and controllers, yielding 18 features per frame).

The following table summarizes the dataset structure as described in (Schach et al., 10 Sep 2025):

Attribute	Description	Example Value
Participants	Number of unique users recorded	49
Applications	Distinct XR applications per user	5 (games + social XR)
Recording Duration	Total hours of captured data	>60 hours
Sampling Rate	Data frequency per frame	30 Hz
Feature Vector	Features per motion frame (HMD + controllers)	18

This setup ensures the feasibility of comparing user motion fingerprints both within a given application and across divergent task environments, capturing intrinsic behavioral invariances and context-specific adaptations.

2. Data Encoding, Preprocessing, and Consistency

To render motion features comparable and to prevent overfitting to extrinsic spatial cues, motion data is preprocessed using normalization schemes such as Body-Relative-Velocity (BRV). Raw tracking signals (positions, rotations) are first resampled to a common temporal grid and then transformed into body-relative coordinates, followed by computation of first-order derivatives to acquire velocity encodings. This approach has been shown (see (Schach et al., 10 Sep 2025, Rack et al., 2023)) to improve model generalization by filtering out application- or room-specific constant offsets, focusing the model's attention on user-typical movement dynamics.

The BRV encoding pipeline is essential for cross-application datasets as it decouples individual behavior from scene artifacts, providing a robust substrate for embedding-based or classification-based identification algorithms.

3. Deep Learning Methodologies for User Identification

Motion-based user identification on cross-application datasets typically employs two types of deep learning methodologies:

• Similarity-Learning Models:

These models generate a latent embedding for each motion sequence, mapping samples from the same user close together while maximizing inter-user distances. Architectures often leverage sequential layers (e.g., GRUs, Transformers) trained on a metric learning loss such as cosine similarity or triplet loss. At inference, identity matching is performed via nearest-neighbor search in the latent space (Schach et al., 10 Sep 2025).

• Classification-Learning Models:

Here, user identity is formulated as a multi-class classification problem with a deep network outputting probabilities over all known users. Training requires fixed user vocabularies and cannot generalize to unseen users without retraining (Rack et al., 2023).

Evaluation on cross-application data reveals that, within the same application, both approaches can yield high identification accuracy (nearest-embedding top-1: ~83%, sequence-level up to ~100%), but generalization across applications declines markedly (top-1: ~18%, top-3: ~56%), revealing a significant “domain gap” induced by task, context, and interaction style (Schach et al., 10 Sep 2025).

4. Generalization, Limitations, and Privacy Considerations

The primary research focus for cross-application XR motion datasets lies in quantifying and improving the generalization of identification models across divergent application domains. Although user-specific patterns persist across contexts, the shift in movement distribution and interaction requirements results in degraded accuracy when reference and query samples originate from different applications. This phenomenon is exacerbated in datasets with high contextual variability, as indicated by the drop in cross-domain accuracy (Schach et al., 10 Sep 2025).

A critical implication is the limited suitability of current models for seamless, application-invariant biometric authentication. However, even modest cross-application recognition rates pose privacy risks: adversaries may de-anonymize users with moderate probability by linking behavioral data across sessions and apps. Prior work has demonstrated that short segments of XR motion data are sufficient for high-confidence user identification within homogenous tasks (~95% in 100 s, (Nair et al., 2023)), but generalizing this across all application types requires further innovation.

5. Applications and Research Implications

Cross-application XR motion datasets serve multiple research and engineering purposes:

• Benchmarking: They provide a rigorous foundation for testing the robustness and transferability of biometric user identification systems for XR and metaverse platforms.
• Algorithmic Development: They enable the development of models that explicitly account for domain shifts (e.g., domain adaptation, meta-learning approaches).
• Risk Assessment: They facilitate empirical investigations into potential privacy breaches, quantifying the probability of unwanted cross-application re-identification.
• Security and Personalization: They inform the design of biometric authentication strategies for live XR systems and guide adaptation strategies for personalized XR content delivery.

Because these datasets are often released alongside their codebases and documentation (see (Schach et al., 10 Sep 2025)), they support open and reproducible research in the growing field of XR behavioral biometrics.

6. Data Accessibility and Community Use

The dataset and corresponding code for collection, preprocessing (including BRV encoding), and model evaluation are made freely available through public repositories. Documentation details file formats, preprocessing steps, and recommended evaluation protocols. Researchers are encouraged to leverage these resources to replicate results, develop novel approaches for cross-application identification, and assess emerging privacy and security frameworks. Such open contributions accelerate advancement and transparency in the broader XR research community.

7. Outlook and Future Directions

Current cross-application XR motion datasets highlight substantial open challenges related to generalization and privacy. Future research directions include:

• Developing models robust to task and context variability using advanced transfer learning or adversarial training schemes.
• Extending datasets to include even more diverse social and collaborative XR applications, as well as additional sensor modalities (e.g., physiological or eye-tracking).
• Incorporating privacy-preserving mechanisms (such as differential privacy) to mitigate inference risks while supporting legitimate identification and personalization use cases (Nair et al., 2023).
• Promoting international standards for data collection, feature representation, and evaluation to facilitate cross-institutional comparison.

In sum, cross-application XR motion datasets are a cornerstone for the empirical paper of behavioral biometrics in XR; they substantiate both performance limitations and privacy vulnerabilities, and their ongoing evolution is pivotal for secure, user-centric metaverse ecosystems.