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We present THUNDR, a transformer-based deep neural network methodology toreconstruct the 3d pose and shape of people, given monocular RGB images. Key toour methodology is an intermediate 3d marker representation, where we aim tocombine the predictive power of model-free-output architectures and theregularizing, anthropometrically-preserving properties of a statistical humansurface model like GHUM -- a recently introduced, expressive full bodystatistical 3d human model, trained end-to-end. Our novel transformer-basedprediction pipeline can focus on image regions relevant to the task, supportsself-supervised regimes, and ensures that solutions are consistent with humananthropometry. We show state-of-the-art results on Human3.6M and 3DPW, for boththe fully-supervised and the self-supervised models, for the task of inferring3d human shape, joint positions, and global translation. Moreover, we observevery solid 3d reconstruction performance for difficult human poses collected inthe wild.

THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers