  This paper presents an inverse kinematic optimization layer (IKOL) for 3D
human pose and shape estimation that leverages the strength of both
optimization- and regression-based methods within an end-to-end framework. IKOL
involves a nonconvex optimization that establishes an implicit mapping from an
image's 3D keypoints and body shapes to the relative body-part rotations. The
3D keypoints and the body shapes are the inputs and the relative body-part
rotations are the solutions. However, this procedure is implicit and hard to
make differentiable. So, to overcome this issue, we designed a Gauss-Newton
differentiation (GN-Diff) procedure to differentiate IKOL. GN-Diff iteratively
linearizes the nonconvex objective function to obtain Gauss-Newton directions
with closed form solutions. Then, an automatic differentiation procedure is
directly applied to generate a Jacobian matrix for end-to-end training.
Notably, the GN-Diff procedure works fast because it does not rely on a
time-consuming implicit differentiation procedure. The twist rotation and shape
parameters are learned from the neural networks and, as a result, IKOL has a
much lower computational overhead than most existing optimization-based
methods. Additionally, compared to existing regression-based methods, IKOL
provides a more accurate mesh-image correspondence. This is because it
iteratively reduces the distance between the keypoints and also enhances the
reliability of the pose structures. Extensive experiments demonstrate the
superiority of our proposed framework over a wide range of 3D human pose and
shape estimation methods.
