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Understanding the camera wearer's activity is central to egocentric vision,yet one key facet of that activity is inherently invisible to the camera--thewearer's body pose. Prior work focuses on estimating the pose of hands and armswhen they come into view, but this 1) gives an incomplete view of the full bodyposture, and 2) prevents any pose estimate at all in many frames, since thehands are only visible in a fraction of daily life activities. We propose toinfer the "invisible pose" of a person behind the egocentric camera. Given asingle video, our efficient learning-based approach returns the full body 3Djoint positions for each frame. Our method exploits cues from the dynamicmotion signatures of the surrounding scene--which changes predictably as afunction of body pose--as well as static scene structures that reveal theviewpoint (e.g., sitting vs. standing). We further introduce a novel energyminimization scheme to infer the pose sequence. It uses soft predictions of theposes per time instant together with a non-parametric model of human posedynamics over longer windows. Our method outperforms an array of possiblealternatives, including deep learning approaches for direct pose regressionfrom images.

Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video