  We present Ego-Exo4D, a diverse, large-scale multimodal multiview video
dataset and benchmark challenge. Ego-Exo4D centers around
simultaneously-captured egocentric 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 unprecedented: 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. Project page:
http://ego-exo4d-data.org/
