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.

本文提出一种基于深度神经网络的人体三维姿态和形状重建方法，使用全身统计 3D 人体模型和学习技巧来优化模型并将其应用于自监督模式下的姿态和形状状态重建，并基于新颖的循环方式更新姿态和形状参数，从而实现了优化损失和度量反差的统一，测试结果表明其在 H3.6M 和 3DPW 等数据集上均获得了极具竞争力的结果。