Ego4D: Around the World in 3,000 Hours of Egocentric Video
This provides a foundational resource for the computer vision and AI research community to advance egocentric video understanding, though it is incremental in scaling up existing dataset efforts.
The authors introduced Ego4D, a massive-scale egocentric video dataset with 3,670 hours of daily-life activity footage from 931 camera wearers across 74 locations, aiming to expand publicly available diverse egocentric video for research. They also presented new benchmark challenges for first-person perception tasks like memory querying and activity forecasting.
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/