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Hands are the central means by which humans manipulate their world and beingable to reliably extract hand state information from Internet videos of humansengaged in their hands has the potential to pave the way to systems that canlearn from petabytes of video data. This paper proposes steps towards this byinferring a rich representation of hands engaged in interaction method thatincludes: hand location, side, contact state, and a box around the object incontact. To support this effort, we gather a large-scale dataset of hands incontact with objects consisting of 131 days of footage as well as a 100Kannotated hand-contact video frame dataset. The learned model on this datasetcan serve as a foundation for hand-contact understanding in videos. Wequantitatively evaluate it both on its own and in service of predicting andlearning from 3D meshes of human hands.

Understanding Human Hands in Contact at Internet Scale