Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
This dataset addresses the need for diverse, real-world video data for AI research in understanding skilled activities, though it is incremental as it builds on existing dataset efforts.
The authors introduced Ego-Exo4D, a large-scale multimodal dataset of skilled human activities captured from both first- and third-person perspectives, comprising 1,286 hours of video from 740 participants across 13 cities, to advance research in video understanding.
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/