Monocular 3D human pose and shape estimation is challenging due to the many
degrees of freedom of the human body and thedifficulty to acquire training data
for large-scale supervised learning in complex visual scenes. In this paper we
present practical semi-supervised and self-supervised models that support
training and good generalization in real-world images and video. Our
formulation is based on kinematic latent normalizing flow representations and
dynamics, as well as differentiable, semantic body part alignment loss
functions that support self-supervised learning. In extensive experiments using
3D motion capture datasets like CMU, Human3.6M, 3DPW, or AMASS, as well as
image repositories like COCO, we show that the proposed methods outperform the
state of the art, supporting the practical construction of an accurate family
of models based on large-scale training with diverse and incompletely labeled
image and video data.

本文提出了一种基于运动捕捉数据集和大规模图像和视频数据集的半监督和自监督学习方法，利用运动和不同 iable 的语义身体部位对齐损失函数进行训练，来精确地估计单眼三维人体姿态和形状。实验结果证明，该方法优于现有技术，可用于广泛的真实场景。