Aria Everyday Activities Dataset
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909116542222336 |
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| author | Lv, Zhaoyang Charron, Nicholas Moulon, Pierre Gamino, Alexander Peng, Cheng Sweeney, Chris Miller, Edward Tang, Huixuan Meissner, Jeff Dong, Jing Somasundaram, Kiran Pesqueira, Luis Schwesinger, Mark Parkhi, Omkar Gu, Qiao De Nardi, Renzo Cheng, Shangyi Saarinen, Steve Baiyya, Vijay Zou, Yuyang Newcombe, Richard Engel, Jakob Julian Pan, Xiaqing Ren, Carl |
| author_facet | Lv, Zhaoyang Charron, Nicholas Moulon, Pierre Gamino, Alexander Peng, Cheng Sweeney, Chris Miller, Edward Tang, Huixuan Meissner, Jeff Dong, Jing Somasundaram, Kiran Pesqueira, Luis Schwesinger, Mark Parkhi, Omkar Gu, Qiao De Nardi, Renzo Cheng, Shangyi Saarinen, Steve Baiyya, Vijay Zou, Yuyang Newcombe, Richard Engel, Jakob Julian Pan, Xiaqing Ren, Carl |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aria Everyday Activities Dataset Lv, Zhaoyang Charron, Nicholas Moulon, Pierre Gamino, Alexander Peng, Cheng Sweeney, Chris Miller, Edward Tang, Huixuan Meissner, Jeff Dong, Jing Somasundaram, Kiran Pesqueira, Luis Schwesinger, Mark Parkhi, Omkar Gu, Qiao De Nardi, Renzo Cheng, Shangyi Saarinen, Steve Baiyya, Vijay Zou, Yuyang Newcombe, Richard Engel, Jakob Julian Pan, Xiaqing Ren, Carl Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction 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. |
| title | Aria Everyday Activities Dataset |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.13349 |