Ego-Exo4D

SCENARIOS

5035

(# takes)

740

(# participants)

123

(# sites)

1286.3

(# ego+exo hours)

Cooking

678

173

564.13h

Music

276

180.08h

Soccer

282

66.97h

Health

397

122

114.5h

Basketball

910

113

78.01h

Dance

728

106.57h

Bike Repair

363

82.15h

Rock Climbing

1401

93.90h

QUESTIONS / ANSWERS

Where can I find additional information?

For further detailed information refer to our manuscript:

Journal Version. K Grauman et al (2025). Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives. International Journal of Computer Vision. Open Access | PDF
K Grauman et al (2024). Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives. IEEE/CVF Computer Vision and Pattern Recognition (CVPR). | Supp Material | ArXiv paper

How can I download the dataset?

The dataset is now publicly available. Please refer to the Getting Started page on the Documentation Site for details on how to download the dataset. To summarize you will need to:

Sign the Ego-Exo4D Licenses, this will take 2 days to be approved.
Download the data via the CLI downloader. Click here for docs.

I have already downloaded and signed Ego4D licenses before. Do I need to sign again?

Yes. As new university partners are part of this effort, a new license should be signed.

Who collected this data?

The data was collected from 740 participants. We showcase a distribution of age and gender from our participants who volunteered to self-identify their demographics — age and gender.

Does the data contain identifying information of individuals?

Yes. Since the scenarios allow for closed environments (e.g., no passerby) nearly all video is available without de-identification. Refer to our paper for details of our privacy and data collection pipeline.

What coverage of scenarios do you have?

A sample visualisation of our scenarios is below.

What metadata, features and models are available with the dataset?

We will be releasing additional information with the dataset before the end of the year

How does this dataset differ from Ego4D?

Ego4D collected unscripted daily life activities. Instead, we here focus on skilled activities specifically, ensuring participants of diverse levels of skill are recorded performing the same scenario, while maintaining distinctions natural to the scenario being carried out in a new environment. We cover both procedural and physical skilled activities. While some of these general scenarios have been captured in Ego4D (there are indeed examples of cooking, climbing or soccer), these are disjoint and do not record the same scenario making them unsuitable for tasks related to to keystep recognition or skill assessment.

Importantly, Ego-Exo4D is a multi-view dataset with synchronized egocentric and multiple exocentric view. This is the largest and most diverse effort for multi-view ego-exo data.

The egocentric views are captured using the Aria Project devices.