  3D whole-body human mesh recovery aims to reconstruct the 3D human body,
face, and hands from a single image. Although powerful deep learning models
have achieved accurate estimation in this task, they require enormous memory
and computational resources. Consequently, these methods can hardly be deployed
on resource-limited edge devices. In this work, we propose a Binarized Dual
Residual Network (BiDRN), a novel quantization method to estimate the 3D human
body, face, and hands parameters efficiently. Specifically, we design a basic
unit Binarized Dual Residual Block (BiDRB) composed of Local Convolution
Residual (LCR) and Block Residual (BR), which can preserve full-precision
information as much as possible. For LCR, we generalize it to four kinds of
convolutional modules so that full-precision information can be propagated even
between mismatched dimensions. We also binarize the face and hands
box-prediction network as Binaried BoxNet, which can further reduce the model
redundancy. Comprehensive quantitative and qualitative experiments demonstrate
the effectiveness of BiDRN, which has a significant improvement over
state-of-the-art binarization algorithms. Moreover, our proposed BiDRN achieves
comparable performance with full-precision method Hand4Whole while using just
22.1% parameters and 14.8% operations. We will release all the code and
pretrained models.
