Abstract:First-person video highlights a camera-wearer's activities in the context of their persistent environment. However, current video understanding approaches reason over visual features from short video clips that are detached from the underlying physical space and only capture what is directly seen. We present an approach that links egocentric video and camera pose over time by learning representations that are predictive of the camera-wearer's (potentially unseen) local surroundings to facilitate human-centric … Show more
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