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Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Abstract
We present Ego-Exo4D, a diverse, large-scale multi-modal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured ego-centric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is un-precedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions-including a novel “expert commentary” done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community.
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“we curate TransferBench -an evaluation dataset consisting of 600 examples across three key scenarios: robotic arm operations, driving, and ego-centric everyday life scenes, each representing a critical aspect of Physical AI.”
From Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control (2025) · cites this paper as Grauman et al., 2024
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Self-citationEgocentric Video-Language Pretraining
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Self-citationA Light Weight Model for Active Speaker Detection
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Self-citationCosmos World Foundation Model Platform for Physical AI
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Self-citation
Metadata
- Authors
- Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., Byrne, E., Chavis, Z., Chen, J., Cheng, F., Chu, F.-J., Crane, S., Dasgupta, A., Dong, J., Escobar, M., Forigua, C., Gebreselasie, A., Haresh, S., Huang, J., Islam, M. M., Jain, S., Khirodkar, R., Kukreja, D., Liang, K. J., Liu, J.-W., Majumder, S., Mao, Y., Martin, M., Mavroudi, E., Nagarajan, T., Ragusa, F., Ramakrishnan, S. K., Seminara, L., Somayazulu, A., Song, Y., Su, S., Xue, Z., Zhang, E., Zhang, J., Castillo, A., Chen, C., Fu, X., Furuta, R., González, C., Gupta, P., Hu, J., Huang, Y., Huang, Y., Khoo, W., Kumar, A., Kuo, R., Lakhavani, S., Liu, M., Luo, M., Luo, Z., Meredith, B., Miller, A., Oguntola, O., Pan, X., Peng, P., Pramanick, S., Ramazanova, M., Ryan, F., Shan, W., Somasundaram, K., Song, C., Southerland, A., Tateno, M., Wang, H., Wang, Y., Yagi, T., Yan, M., Yang, X., Yu, Z., Zha, S. C., Zhao, C., Zhao, Z., Zhu, Z., Zhuo, J., Arbeláez, P., Bertasius, G., Damen, D., Engel, J., Maria Farinella, G., Furnari, A., Ghanem, B., Hoffman, J., Jawahar, C. V., Newcombe, R., Park, H. S., Rehg, J. M., Sato, Y., Savva, M., Shi, J., Shout, M. Z., & Wray, M.
- Affiliations
- FAIR, Meta
- Project Aria, Meta
- University of Bristol
- University of Illinois,Urbana Champaign
- University of Minnesota
- National University of Singapore
- Carnegie Mellon University
- International Institute of Information Technology,Hyderabad
- Universidad de los Andes
- Simon Fraser University
- University of North Carolina,Chapel Hill
- University of Catania
- University of Texas at Austin
- University of Pennsylvania
- University of Tokyo
- Meta
- Indiana University
- Johns Hopkins University
- King Abdullah University of Science and Technology
- Georgia Tech
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2024-06-16
- Pages
- 19383-19400
- Cited by
- 182
- References
- 178
- Funders
- NSF (CNS-2308994,DRL-2112635)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
