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XR Motion Dataset Catalogue
Overview
The XR Motion Dataset Catalogue, accompanying our paper "Navigating the Kinematic Maze: A Comprehensive Guide to XR Motion Dataset Standards," standardizes and simplifies access to Extended Reality (XR) motion datasets. The catalogue represents our initiative to streamline the usage of kinematic data in XR research by aligning various datasets to a consistent format and structure.
Dataset Specifications
All datasets in this catalogue have been standardized with the following specifications:
- Coordinate System: X (Right), Y (Up), Z (Forward)
- Rotation Representation: Quaternions
- Units of Measurement: Centimeters for spatial data
- Time Encoding: Milliseconds for time-related data
These specifications ensure uniformity and comparability across all datasets in the catalogue.
Conversion Scripts Repository
The alignment of datasets was facilitated by a series of conversion scripts, which are available in our GitHub repository: XR Motion Dataset Conversion Scripts. These scripts detail the process of aligning attribute names, coordinate systems, rotation representations, units of measurement, and time encoding.
Included Datasets
The catalogue includes the following datasets:
- LiebersBeatSaber23
- Boxrr23 – edit 2024-05-04: we are still working on providing the aligned version – in the meantime you find the original version here
- BOXRR24 – WIP: we are currently working on the next version of the BOXRR-23 dataset, which will include significantly more user – we do our best to make it available later this year
- LiebersHand22
- LiebersLabStudy21
- MooreCrossDomain23
RMillerBall22request for permissions pending- VrNet
- WhoIsAlyx
Installation and Usage
Loading the Dataset with Hugging Face datasets Library
To load a dataset from the catalogue, use the datasets library in Python. For example, to load the WhoIsAlyx dataset:
from datasets import load_dataset
dataset = load_dataset("cschell/xr-motion-dataset-catalogue", "who_is_alyx", trust_remote_code=True)
Loading Individual Recordings with Pandas
To load individual recordings, you can use pandas. Here's an example:
import pandas as pd
file_url_path = "hf://datasets/cschell/xr-motion-dataset-catalogue/who_is_alyx/player_02/2022-01-07.parquet"
recording = pd.read_parquet(file_url_path)
Contributing and Feedback
Contributions and feedback are welcome to enhance the XR Motion Dataset Catalogue. Feel free to open a pull request or contact us directly.
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