面向服装个性化定制的多视角轮廓三维人体快速重建方法  

Toward Clothing Personalized Customization Multi-Perspective Silhouettes 3D Human Body Rapid Reconstruction

作　　者：张树有[1] 房乃玉 裘乐淼[1] 刘艺舒 王自立[1] Zhang Shuyou;Fang Naiyu;Qiu Lemiao;Liu Yishu;Wang Zili(State Key Laboratory of Fluid Power Transmission&Control,Zhejiang University,HangZhou 310027)

机构地区：[1]浙江大学流体动力与机电系统国家重点实验室,杭州310027

出　　处：《计算机辅助设计与图形学学报》2022年第11期1753-1762,共10页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家重点研发计划“互联网+”产品定制设计方法与技术(2018YFB1700700)。

摘　　要：服装个性化在线定制需要围绕用户形状参数进行设计,传统的量体方法获取的用户形状参数误差高、反馈慢,亟需构建一种三维人体重建方法能够快速感知用户人体形状.针对此问题,提出了一种面向服装个性化定制的多视角轮廓三维人体快速重建方法.利用多级空洞卷积分割网络(multilevel dilated convolution semantic network,MDS-Net)提取人体轮廓图像中整体和局部特征,实现轮廓图像的语义分割;利用躯干参数提取网络(torso parameter extraction network,TPE-Net)提取多视角人体轮廓分割图的形状和姿势参数;利用主成分分析(principal component analysis,PCA)提取三维人体模型潜层空间的语义特征,并映射为由TPE-Net输出的形状和姿势参数,从而实现三维人体重建.在PyTorch环境下,采用4个数据集进行实验验证,结果表明,MDS-Net在测试集上的分割mIoU评分平均为0.881,能够实现整体分割和局部细节保留;TPE-Net在测试集上形状参数预测准确率为0.74,关节预测偏移距离与运动树中的索引呈正比;同时,使用真实案例验证了整个三维人体重建方法的有效性.The clothing personalized online customization is required to design around the user shape parameters,and the traditional anthropometry method obtains the user shape parameter with a low speed and a high error.To tackle this issue,a rapid 3D human body reconstruction method is proposed based on multi-perspective silhouettes for clothing personalized customization.The multilevel dilated convolution semantic network(MDS-Net)is leveraged to extract the global and local features in the human silhouettes to implement the semantic segmentation.The torso parameter extraction network(TPE-Net)is leveraged to extract the shape and pose parameters of the multi-perspective human body segmentation maps.The principal component analysis(PCA)is leveraged to extract the semantic features in the latent space of the 3D human body model,and the semantic features are mapped to the shape and posture parameters output by the torso parameter extraction network,thus,3D human body model is reconstrued.This method was verified in a PyTorch environment with four datasets.The experiments demonstrated that the mIoU of the multilevel dilated convolution semantic network on the test set is 0.881,which can achieve overall segmentation and local detail preservation.The torso parameter extraction network has an accuracy of 0.74 in the shape parameter prediction on the test set,the prediction of joint offset distance is proportional to the index in the kinematic tree.The entire 3D human body reconstruction method is verified by the real cases.

关 键 词：三维人体重建 服装个性化定制 多视角轮廓 人体特征 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]