We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D.
To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.


News & Updates

27th March 2017

Update: A small bug fix in the visualization code of SMPLify fits.

9th October 2016

The SMPLify website is now live!.

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Referencing the Code

        title = {Keep it {SMPL}: Automatic Estimation of {3D} Human Pose and Shape
        from a Single Image},
        author = {Bogo, Federica and Kanazawa, Angjoo and Lassner, Christoph and
        Gehler, Peter and Romero, Javier and Black, Michael J.},
        booktitle = {Computer Vision -- ECCV 2016},
        series = {Lecture Notes in Computer Science},
        publisher = {Springer International Publishing},
        month = oct,
        year = {2016}