ResXR

An open-source, end-to-end toolkit for conducting behavioral XR experiments on Meta standalone head-mounted displays

Addressing technical barriers and data standardization in XR research

About ResXR

The Challenge

Widespread adoption of XR in behavioral research is hindered by high technical barriers and the absence of standardized data formats. Creating immersive experiments demands specialized programming skills, and researchers lack accessible templates designed for scientific applications.

The Solution

ResXR provides a Unity-based experiment template for multimodal data capture alongside a Python processing pipeline that automates validation, preprocessing, and quality reporting, inspired by established neuroimaging tools like fMRIPrep.

Target Platform

Meta standalone headsets (Quest 2, Quest Pro, Quest 3)

Output Format

Motion-BIDS compatible data for reproducible research

ResXR Architecture

ResXR comprises four hierarchical stages spanning Unity-based data collection and Python-based post-processing

Stage 0

Base Template

Available

Unity-based experiment template for multimodal data capture

Head, hand, eye, and face tracking
ResXRPlayer, ResXREyeTracker, ResXRDataManager components
Automatic trial logging and synchronized data collection

Demo Experiments Included:

Binary Choice (2AFC paradigm with paired visual stimuli)
Maze Navigation (spatial wayfinding task with trajectory capture)
Museum Viewing (free-exploration gallery for eye-tracking studies)

View on GitHub →

Stage 1

Data Validity

Coming Soon

Quality checks and validation

Configurable quality checks (e.g., tracking loss detection)
Quality flags generation
Automated validation pipeline

Stage 2

Data Filtration

Coming Soon

Preprocessing pipeline

Excludes flagged segments
Computes derived measures
Automated preprocessing workflow

Stage 3

Data Presentation

Coming Soon

Reports and export

Comprehensive HTML reports with diagnostic plots
Analysis-ready export
Visual quality assurance reports