Previous 3D human pose and mesh estimation methods mostly rely on only global image feature to predict 3D rotations of human joints (i.e., 3D rotational pose) from an input image. However, local features on the position of human joints (i.e., positional pose) can provide joint-specific information, which is essential to understand human articulation. To effectively utilize both local and global features, we present Pose2Pose, a 3D positional pose-guided 3D rotational pose prediction network, along with a positional pose-guided pooling and joint-specific graph convolution. The positional pose-guided pooling extracts useful joint-specific local and global features. Also, the joint-specific graph convolution effectively processes the joint-specific features by learning joint-specific characteristics and different relationships between different joints. We use Pose2Pose for expressive 3D human pose and mesh estimation and show that it outperforms all previous part-specific and expressive methods by a large margin. The codes will be publicly available.

本研究提出了Hand4Whole算法，使用Pose2Pose模块识别3D节旋转，并使用MCP关节特征预测3D腕部。另外，在预测3D手指旋转时，舍弃了人体特征，经过端到端训练后，相比之前的基于全身3D人体网格估计方法，我们的算法能够产生更好的3D手部重建结果。

全身3D人体网格估计的准确3D手部姿势估计