Aria Everyday Activities Dataset
<|reference_start|>Aria Everyday Activities Dataset: We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data including high frequency globally aligned 3D trajectories, scene point cloud, per-frame 3D eye gaze vector and time aligned speech transcription. In this paper, we demonstrate a few exemplar research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation. AEA is an open source dataset that can be downloaded from https://www.projectaria.com/datasets/aea/. We are also providing open-source implementations and examples of how to use the dataset in Project Aria Tools https://github.com/facebookresearch/projectaria_tools.<|reference_end|>
@article{lv2024aria,
    title={Aria Everyday Activities Dataset},
    author={Zhaoyang Lv, Nicholas Charron, Pierre Moulon, Alexander Gamino, Cheng
  Peng, Chris Sweeney, Edward Miller, Huixuan Tang, Jeff Meissner, Jing Dong,
  Kiran Somasundaram, Luis Pesqueira, Mark Schwesinger, Omkar Parkhi, Qiao Gu,
  Renzo De Nardi, Shangyi Cheng, Steve Saarinen, Vijay Baiyya, Yuyang Zou,
  Richard Newcombe, Jakob Julian Engel, Xiaqing Pan, Carl Ren},
    journal={arXiv preprint arXiv:2402.13349},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13349},
    primaryClass={cs.CV cs.AI cs.HC}
}