65 sessions across 422 segments with 4,849 files provide synchronized pose trajectories for EdgeXR and VR research. The dataset includes temporally and spatially aligned pose data captured at 500 Hz from SteamVR gaming sessions via OpenXR API readings and marker-based optical motion capture. Ziyu Zhong organized the data into a cleaner structure and prepared it for confidential peer review on Harvard Dataverse.
Use Cases
- Benchmark evaluation for head pose prediction model development based on synchronized pose trajectories
- Head pose estimation quality analysis and accuracy assessment based on cross-modal data
- Cross-modal synchronization and calibration between OpenXR and mocap systems based on aligned data
- Motion predictability quantification and classification based on high-frequency pose trajectories
- VR tracking latency analysis and mitigation strategies based on temporally aligned capture modalities
Strengths
- 65 sessions across 422 segments provide a substantial collection of VR pose data
- 4,849 files offer temporally and spatially aligned pose data captured at 500 Hz (2 ms resolution)
- Synchronized across two complementary capture modalities: OpenXR API readings and marker-based optical motion capture
Limitations
- Column-level documentation is absent; field semantics must be inferred after download
- Row count is unknown, which may limit suitability assessment
- Last updated 2026-07-14 13:19:51; freshness should be verified
Provenance
- Source
- Ziyu Zhong Dataverse
- Collection Method
- Pose trajectories captured from SteamVR gaming sessions via OpenXR API readings and marker-based optical motion capture.