from pathlib import Path import datasets from datasets import ArrowBasedBuilder class KinematicXRDatasetsBaseConfig(datasets.BuilderConfig): features = datasets.Features( { "user": datasets.Value("string"), "session": datasets.Value("string"), "delta_time_ms": datasets.Value("float32"), "head_pos_x": datasets.Value("float32"), "head_pos_y": datasets.Value("float32"), "head_pos_z": datasets.Value("float32"), "head_rot_x": datasets.Value("float32"), "head_rot_y": datasets.Value("float32"), "head_rot_z": datasets.Value("float32"), "head_rot_w": datasets.Value("float32"), "left_hand_pos_x": datasets.Value("float32"), "left_hand_pos_y": datasets.Value("float32"), "left_hand_pos_z": datasets.Value("float32"), "left_hand_rot_x": datasets.Value("float32"), "left_hand_rot_y": datasets.Value("float32"), "left_hand_rot_z": datasets.Value("float32"), "left_hand_rot_w": datasets.Value("float32"), "right_hand_pos_x": datasets.Value("float32"), "right_hand_pos_y": datasets.Value("float32"), "right_hand_pos_z": datasets.Value("float32"), "right_hand_rot_x": datasets.Value("float32"), "right_hand_rot_y": datasets.Value("float32"), "right_hand_rot_z": datasets.Value("float32"), "right_hand_rot_w": datasets.Value("float32"), } ) citation = None homepage = None name = None license = "CC BY-NC-SA 4.0" def __init__(self, **kwargs): super().__init__(name=self.name, version=datasets.Version("1.0.0"), **kwargs) def check_permissions(self): return True class LiebersBeatSaber23Config(KinematicXRDatasetsBaseConfig): name = "liebers_beat_saber_23" citation = """\ @inproceedings{10.1145/3611659.3615696, author = {Liebers, Jonathan and Burschik, Christian and Gruenefeld, Uwe and Schneegass, Stefan}, title = {Exploring the Stability of Behavioral Biometrics in Virtual Reality in a Remote Field Study: Towards Implicit and Continuous User Identification through Body Movements}, year = {2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3611659.3615696}, doi = {10.1145/3611659.3615696}, abstract = {Behavioral biometrics has recently become a viable alternative method for user identification in Virtual Reality (VR). Its ability to identify users based solely on their implicit interaction allows for high usability and removes the burden commonly associated with security mechanisms. However, little is known about the temporal stability of behavior (i.e., how behavior changes over time), as most previous works were evaluated in highly controlled lab environments over short periods. In this work, we present findings obtained from a remote field study (N = 15) that elicited data over a period of eight weeks from a popular VR game. We found that there are changes in people’s behavior over time, but that two-session identification still is possible with a mean F1-score of up to 71\%, while an initial training yields 86\%. However, we also see that performance can drop by up to over 50 percentage points when testing with later sessions, compared to the first session, particularly for smaller groups. Thus, our findings indicate that the use of behavioral biometrics in VR is convenient for the user and practical with regard to changing behavior and also reliable regarding behavioral variation.}, booktitle = {Proceedings of the 29th ACM Symposium on Virtual Reality Software and Technology}, articleno = {30}, numpages = {12}, location = {, Christchurch, New Zealand, }, series = {VRST '23} }""" class Boxrr23Config(KinematicXRDatasetsBaseConfig): name = "boxrr23" citation = """\ @misc{BOXRR-23, title = {Berkeley {Open} {Extended} {Reality} {Recording} {Dataset} 2023 ({BOXRR}-23)}, url = {https://rdi.berkeley.edu/metaverse/boxrr-23/}, doi = {10.25350/B5NP4V}, urldate = {2023-07-11}, publisher = {University of California, Berkeley}, author = {{Vivek Nair} and {Wenbo Guo} and {Rui Wang} and {James F. O'Brien} and {Louis Rosenberg} and {Dawn Song}}, year = {2023} }""" features = datasets.Features({"app": datasets.Value("string"), **KinematicXRDatasetsBaseConfig.features}) def check_permissions(self): import warnings warnings.warn( f"""\ BOXRR-23 is very large, and we haven't managed to upload the complete dataset yet. Instead, you are now downloading a small subset of a few users only as example.""" ) class LiebersHand22Config(KinematicXRDatasetsBaseConfig): name = "liebers_hand22" citation = """\ @article{Liebers2022, title = {Identifying {Users} by {Their} {Hand} {Tracking} {Data} in {Augmented} and {Virtual} {Reality}}, issn = {1044-7318}, url = {https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2120845}, doi = {10.1080/10447318.2022.2120845}, abstract = {Nowadays, Augmented and Virtual Reality devices are widely available and are often shared among users due to their high cost. Thus, distinguishing users to offer personalized experiences is essenti...}, journal = {International Journal of Human–Computer Interaction}, author = {Liebers, Jonathan and Brockel, Sascha and Gruenefeld, Uwe and Schneegass, Stefan}, month = oct, year = {2022}, pages = {1--16} }""" features = datasets.Features( { "xr": datasets.Value("string"), "scene": datasets.Value("string"), **KinematicXRDatasetsBaseConfig.features, } ) class LiebersLabStudy21Config(KinematicXRDatasetsBaseConfig): name = "liebers_lab_study21" citation = """\ @inproceedings{Liebers2021, address = {New York, NY, USA}, title = {Understanding user identification in virtual reality through behavioral biometrics and the effect of body normalization}, isbn = {978-1-4503-8096-6}, url = {https://dl.acm.org/doi/10.1145/3411764.3445528}, doi = {10.1145/3411764.3445528}, abstract = {Virtual Reality (VR) is becoming increasingly popular both in the entertainment and professional domains. Behavioral biometrics have recently been investigated as a means to continuously and implicitly identify users in VR. Applications in VR can specifcally beneft from this, for example, to adapt virtual environments and user interfaces as well as to authenticate users. In this work, we conduct a lab study (N = 16) to explore how accurately users can be identifed during two task-driven scenarios based on their spatial movement. We show that an identifcation accuracy of up to 90\% is possible across sessions recorded on diferent days. Moreover, we investigate the role of users' physiology in behavioral biometrics by virtually altering and normalizing their body proportions. We fnd that body normalization in general increases the identifcation rate, in some cases by up to 38\%; hence, it improves the performance of identifcation systems.}, booktitle = {Conference on {Human} {Factors} in {Computing} {Systems} - {Proceedings}}, publisher = {Association for Computing Machinery}, author = {Liebers, Jonathan and Abdelaziz, Mark and Mecke, Lukas}, month = may, year = {2021}, pages = {1--11} }""" features = datasets.Features( { "scene": datasets.Value("string"), "norm": datasets.Value("string"), "repetition": datasets.Value("string"), **KinematicXRDatasetsBaseConfig.features, } ) class MooreCrossDomain23Config(KinematicXRDatasetsBaseConfig): name = "moore_cross_domain23" citation = """\ @inproceedings{10.1109/ISMAR59233.2023.00054, title = {Identifying virtual reality users across domain-specific tasks: {A} systematic investigation of tracked features for assembly}, doi = {10.1109/ISMAR59233.2023.00054}, booktitle = {2023 {IEEE} international symposium on mixed and augmented reality ({ISMAR})}, author = {Moore, Alec G. and Do, Tiffany D. and Ruozzi, Nicholas and McMahan, Ryan P.}, year = {2023}, pages = {396--404} }""" features = datasets.Features( { "build": datasets.Value("string"), **{k: v for k, v in KinematicXRDatasetsBaseConfig.features.items() if k != "session"}, } ) class RMillerBall22Config(KinematicXRDatasetsBaseConfig): name = "rmiller_ball22" citation = """\ @inproceedings{miller2022temporal, title={Temporal Effects in Motion Behavior for Virtual Reality (VR) Biometrics}, author={Miller, Robert and Banerjee, Natasha Kholgade and Banerjee, Sean}, booktitle={2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)}, pages={563--572}, year={2022}, organization={IEEE} }""" features = datasets.Features( { "system": datasets.Value("string"), "repetition": datasets.Value("string"), **KinematicXRDatasetsBaseConfig.features, } ) def check_permissions(self): raise Exception( f"""\ Dataset not available yet due to licensing issues – we have contacted the authors and are trying to solve this! In the meantime you can visit the dataset's main repository [1] and convert it with our conversion script. [1] https://github.com/Terascale-All-sensing-Research-Studio/VR-Biometric-Authentication """ ) class VrNetConfig(KinematicXRDatasetsBaseConfig): name = "vr_net" citation = """\ @misc{Wen2023, title = {{VR}.net: {A} {Real}-world {Dataset} for {Virtual} {Reality} {Motion} {Sickness} {Research}}, shorttitle = {{VR}.net}, url = {http://arxiv.org/abs/2306.03381}, abstract = {Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches demand an accurately-labeled, real-world, and diverse dataset for high accuracy and generalizability. As a starting point to address this need, we introduce `VR.net', a dataset offering approximately 12-hour gameplay videos from ten real-world games in 10 diverse genres. For each video frame, a rich set of motion sickness-related labels, such as camera/object movement, depth field, and motion flow, are accurately assigned. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we utilize a tool to automatically and precisely extract ground truth data from 3D engines' rendering pipelines without accessing VR games' source code. We illustrate the utility of VR.net through several applications, such as risk factor detection and sickness level prediction. We continuously expand VR.net and envision its next version offering 10X more data than the current form. We believe that the scale, accuracy, and diversity of VR.net can offer unparalleled opportunities for VR motion sickness research and beyond.}, urldate = {2023-11-03}, publisher = {arXiv}, author = {Wen, Elliott and Gupta, Chitralekha and Sasikumar, Prasanth and Billinghurst, Mark and Wilmott, James and Skow, Emily and Dey, Arindam and Nanayakkara, Suranga}, month = jun, year = {2023}, note = {arXiv:2306.03381 [cs]}, keywords = {Computer Science - Artificial Intelligence} }""" class WhoIsAlyxConfig(KinematicXRDatasetsBaseConfig): name = "who_is_alyx" citation = """\ @article{10.3389/frvir.2023.1272234, author = {Rack, Christian and Fernando, Tamara and Yalcin, Murat and Hotho, Andreas and Latoschik, Marc Erich}, doi={10.3389/frvir.2023.1272234}, journal = {Frontiers in Virtual Reality}, title = {Who is Alyx? A new behavioral biometric dataset for user identification in XR}, volume = {4}, year = {2023} }""" class AlyxDataset(ArrowBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ LiebersBeatSaber23Config(), Boxrr23Config(), LiebersHand22Config(), LiebersLabStudy21Config(), MooreCrossDomain23Config(), RMillerBall22Config(), VrNetConfig(), WhoIsAlyxConfig(), ] def _info(self): self.config.check_permissions() return datasets.DatasetInfo( # description=self.config.description, features=self.config.features, homepage=self.config.homepage, license=self.config.homepage, citation=self.config.citation, ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name="main", gen_kwargs={ "filepath": Path(dl_manager._data_dir or dl_manager._base_path), }, ) ] def _generate_tables(self, filepath): import pandas as pd import pyarrow as pa for filename in list(filepath.glob(f"{self.config.name}/**/*.parquet")): df = pd.read_parquet(filename) yield str(filename), pa.Table.from_pandas(df)