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.
Cite
@article{arxiv.2402.13349,
title = {Aria Everyday Activities Dataset},
author = {Zhaoyang Lv and Nicholas Charron and Pierre Moulon and Alexander Gamino and Cheng Peng and Chris Sweeney and Edward Miller and Huixuan Tang and Jeff Meissner and Jing Dong and Kiran Somasundaram and Luis Pesqueira and Mark Schwesinger and Omkar Parkhi and Qiao Gu and Renzo De Nardi and Shangyi Cheng and Steve Saarinen and Vijay Baiyya and Yuyang Zou and Richard Newcombe and Jakob Julian Engel and Xiaqing Pan and Carl Ren},
journal= {arXiv preprint arXiv:2402.13349},
year = {2024}
}