Aria Gen 2 Pilot Dataset
Not on the map. Why?
Abstract (arXiv)
The Aria Gen 2 Pilot Dataset (A2PD) is an egocentric multimodal open dataset captured using the state-of-the-art Aria Gen 2 glasses. To facilitate timely access, A2PD is released incrementally with ongoing dataset enhancements. The initial release features Dia'ane, our primary subject, who records her daily activities alongside friends, each equipped with Aria Gen 2 glasses. It encompasses five primary scenarios: cleaning, cooking, eating, playing, and outdoor walking. In each of the scenarios, we provide comprehensive raw sensor data and output data from various machine perception algorithms. These data illustrate the device's ability to perceive the wearer, the surrounding environment, and interactions between the wearer and the environment, while maintaining robust performance across diverse users and conditions. The A2PD is publicly available at projectaria.com, with open-source tools and usage examples provided in Project Aria Tools.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 17 Oct 2025.
Builds on
10 of 15 references.
References · 10
- 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
- 2023Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectivescited by 490
- 2024EgoMimic: Scaling Imitation Learning via Egocentric Videocited by 175
- 2025FoundationStereo: Zero-Shot Stereo Matchingcited by 173
- 2023Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perceptioncited by 167
- 2024HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videoscited by 129
- 2024Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wildcited by 114
- 2025HD-EPIC: A Highly-Detailed Egocentric Video Datasetcited by 104
- 2024Aria Everyday Activities Datasetcited by 39
- 2024EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Modelscited by 26
Built on by
Citing papers per year: 2025: 1; 2026: 5.
6 papers.
- 2025Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matchingcited by 28
- 2026Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3Dcited by 7
- 2026LAMP: Localization Aware Multi-camera People Tracking in Metric 3D Worldcited by 1
- 2026EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devicescited by 1
- 2026Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Modelcited by 1
- 2026AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illuminationcited by 0
- 2026AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination2 Jun 2026
- 2026Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Model17 May 2026
- 2026EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices16 May 2026
- 2026LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World6 May 2026
- 2026Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D6 Apr 2026
- 2025Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching11 Dec 2025