基于高分辨率网络和图卷积网络的三维人体重建模型

针对单目图像重建人体时出现的头部姿态翻转和图像特征间隐式空间线索缺失的问题，提出了一种基于高分辨率网络（HRNet）和图卷积网络（GCN）的三维人体重建模型。首先利用HRNet和残差块作为主干网络从原始图像中提取丰富的人体特征信息，然后使用GCN来捕获特征之间隐式的空间线索以获得空间精确的特征表示，最后使用此特征来预测多人线性蒙皮模型（SMPL）的参数以得到更加准确的重建结果；同时为了有效解决人体头部姿态翻转的问题，对SMPL的关节点重新进行了定义，在原有关节的基础上增加对头部关节点的定义。实验结果表明，所提模型能够准确地重建出三维人体，在2D数据集LSP上的重建准确率达到了92.41%，在3D数据集MPI-INF-3DHP上的关节误差和重建误差也大幅降低，平均误差仅分别为97.73 mm和64.63 mm，验证了所提模型在人体重建领域的有效性。

Three-dimensional human reconstruction model based on high-resolution net and graph convolutional network

Focused on the head pose flipping and the implicit spatial cues missing between image features when reconstructing human body from monocular images， a three-dimensional human reconstruction model based on High-Resolution Net （HRNet） and Graph Convolutional Network （GCN） was proposed. Firstly， the rich human feature information was extracted from the original image by using HRNet and residual blocks as the backbone network. Then， the accurate spatial feature representation was obtained by using GCN to capture the implicit spatial cues. Finally， the parameters of Skinned Multi-Person Linear model （SMPL） were predicted by using the features， thereby obtaining more accurate reconstruction results. At the same time， to effectively solve the problem of human head pose flipping， the joint points of SMPL were redefined and the definition of the head joint points were added on the basis of the original joints. Experimental results show that this model can exactly reconstruct the three-dimensional human body. The reconstruction accuracy of this model on the 2D dataset LSP reaches 92.41%， and the joint error and reconstruction error of the model are greatly reduced on the 3D dataset MPI-INF-3DHP with the average of only 97.73 mm and 64.63 mm respectively， verifying the effectiveness of the proposed model in the field of human reconstruction.