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Recovering 3D full-body human pose is a challenging problem with manyapplications. It has been successfully addressed by motion capture systems withbody worn markers and multiple cameras. In this paper, we address the morechallenging case of not only using a single camera but also not leveragingmarkers: going directly from 2D appearance to 3D geometry. Deep learningapproaches have shown remarkable abilities to discriminatively learn 2Dappearance features. The missing piece is how to integrate 2D, 3D and temporalinformation to recover 3D geometry and account for the uncertainties arisingfrom the discriminative model. We introduce a novel approach that treats 2Djoint locations as latent variables whose uncertainty distributions are givenby a deep fully convolutional neural network. The unknown 3D poses are modeledby a sparse representation and the 3D parameter estimates are realized via anExpectation-Maximization algorithm, where it is shown that the 2D jointlocation uncertainties can be conveniently marginalized out during inference.Extensive evaluation on benchmark datasets shows that the proposed approachachieves greater accuracy over state-of-the-art baselines. Notably, theproposed approach does not require synchronized 2D-3D data for training and isapplicable to "in-the-wild" images, which is demonstrated with the MPIIdataset.

MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior