  We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows
us to estimate a human body mesh from the positions of 3D body keypoints. To
this end, we learn to estimate 3D positions of 26 body keypoints and propose an
analytical solution to articulate a parametric body model, SMPL, via a set of
straightforward geometric transformations. Since keypoint estimation directly
relies on image clues, our approach offers significantly better alignment to
image content when compared to state-of-the-art approaches. Our proposed
approach does not require any paired mesh annotations and is able to achieve
state-of-the-art mesh fittings through 3D keypoint regression only. Results on
the challenging 3DPW and Human3.6M demonstrate that our approach yields
state-of-the-art body mesh fittings.
