  We propose a novel algorithm for the fitting of 3D human shape to images.
Combining the accuracy and refinement capabilities of iterative gradient-based
optimization techniques with the robustness of deep neural networks, we propose
a gradient descent algorithm that leverages a neural network to predict the
parameter update rule for each iteration. This per-parameter and state-aware
update guides the optimizer towards a good solution in very few steps,
converging in typically few steps. During training our approach only requires
MoCap data of human poses, parametrized via SMPL. From this data the network
learns a subspace of valid poses and shapes in which optimization is performed
much more efficiently. The approach does not require any hard to acquire
image-to-3D correspondences. At test time we only optimize the 2D joint
re-projection error without the need for any further priors or regularization
terms. We show empirically that this algorithm is fast (avg. 120ms
convergence), robust to initialization and dataset, and achieves
state-of-the-art results on public evaluation datasets including the
challenging 3DPW in-the-wild benchmark (improvement over SMPLify 45%) and also
approaches using image-to-3D correspondences
