Aria Everyday Activities Dataset

TL;DR

The Aria Everyday Activities Dataset is a comprehensive egocentric multimodal dataset capturing daily activities with diverse sensor data, enabling research in neural scene reconstruction and segmentation.

Contribution

This paper introduces the first open-source egocentric multimodal dataset with diverse sensor modalities and multiple research applications.

Findings

Dataset includes 143 activity sequences from diverse indoor locations.

Provides high-frequency 3D trajectories, scene point clouds, eye gaze, and speech data.

Demonstrates applications in neural scene reconstruction and segmentation.

Abstract

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.