Ego-Exo4D: Understanding Skilled Human Activity from First and Third-Person Perspectives

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 cap- tures 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 teach- ers 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 un- derstanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community.