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.

THUNDR 是一种基于变形器的深度神经网络方法，可以重建在单目 RGB 图像中的人体 3D 姿态和形状；其关键在于通过一个中间的 3D 标记表示，通过将无模型输出结构的预测能力与类似于 GHUM 的统计人体表面模型的规则化，人体测量性保持的性质相结合来实现。该方法在人形态和全局转换推断任务中，得到了 Human3.6M 和 3DPW 上的超卓成果，并能够应对野外人体姿势等困难情境。