  We present deep neural network methodology to reconstruct the 3d pose and
shape of people, given an input RGB image. We rely on a recently introduced,
expressivefull body statistical 3d human model, GHUM, trained end-to-end, and
learn to reconstruct its pose and shape state in a self-supervised regime.
Central to our methodology, is a learning to learn and optimize approach,
referred to as HUmanNeural Descent (HUND), which avoids both second-order
differentiation when training the model parameters,and expensive state gradient
descent in order to accurately minimize a semantic differentiable rendering
loss at test time. Instead, we rely on novel recurrent stages to update the
pose and shape parameters such that not only losses are minimized effectively,
but the process is meta-regularized in order to ensure end-progress. HUND's
symmetry between training and testing makes it the first 3d human sensing
architecture to natively support different operating regimes including
self-supervised ones. In diverse tests, we show that HUND achieves very
competitive results in datasets like H3.6M and 3DPW, aswell as good quality 3d
reconstructions for complex imagery collected in-the-wild.
