  We present THUNDR, a transformer-based deep neural network methodology to
reconstruct the 3d pose and shape of people, given monocular RGB images. Key to
our methodology is an intermediate 3d marker representation, where we aim to
combine the predictive power of model-free-output architectures and the
regularizing, anthropometrically-preserving properties of a statistical human
surface model like GHUM -- a recently introduced, expressive full body
statistical 3d human model, trained end-to-end. Our novel transformer-based
prediction pipeline can focus on image regions relevant to the task, supports
self-supervised regimes, and ensures that solutions are consistent with human
anthropometry. We show state-of-the-art results on Human3.6M and 3DPW, for both
the fully-supervised and the self-supervised models, for the task of inferring
3d human shape, joint positions, and global translation. Moreover, we observe
very solid 3d reconstruction performance for difficult human poses collected in
the wild.
