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Existing marker-less motion capture methods often assume known backgrounds,static cameras, and sequence specific motion priors, which narrows itsapplication scenarios. Here we propose a fully automatic method that givenmulti-view video, estimates 3D human motion and body shape. We take recentSMPLify \cite{bogo2016keep} as the base method, and extend it in several ways.First we fit the body to 2D features detected in multi-view images. Second, weuse a CNN method to segment the person in each image and fit the 3D body modelto the contours to further improves accuracy. Third we utilize a generic androbust DCT temporal prior to handle the left and right side swapping issuesometimes introduced by the 2D pose estimator. Validation on standardbenchmarks shows our results are comparable to the state of the art and alsoprovide a realistic 3D shape avatar. We also demonstrate accurate results onHumanEva and on challenging dance sequences from YouTube in monocular case.

Towards Accurate Markerless Human Shape and Pose Estimation over Time