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万云翀, 宋云鹏, 刘利刚. 基于体素联合坐标的单人三维姿态估计[J]. 计算机辅助设计与图形学学报, 2022, 34(9): 1411-1419. DOI: 10.3724/SP.J.1089.2022.19167
引用本文: 万云翀, 宋云鹏, 刘利刚. 基于体素联合坐标的单人三维姿态估计[J]. 计算机辅助设计与图形学学报, 2022, 34(9): 1411-1419. DOI: 10.3724/SP.J.1089.2022.19167
Wan Yunchong, Song Yunpeng, Liu Ligang. 3D Human Pose Estimation Based on Volumetric Joint Coordinates[J]. Journal of Computer-Aided Design & Computer Graphics, 2022, 34(9): 1411-1419. DOI: 10.3724/SP.J.1089.2022.19167
Citation: Wan Yunchong, Song Yunpeng, Liu Ligang. 3D Human Pose Estimation Based on Volumetric Joint Coordinates[J]. Journal of Computer-Aided Design & Computer Graphics, 2022, 34(9): 1411-1419. DOI: 10.3724/SP.J.1089.2022.19167

基于体素联合坐标的单人三维姿态估计

3D Human Pose Estimation Based on Volumetric Joint Coordinates

  • 摘要: 从单幅彩色图像获取三维人体姿态是许多应用的基本任务,但精度不足和不适定姿态难以判断的问题一直存在,因此提出一个基于深度学习的方法处理三维姿态估计的问题.首先,使用空间体素作为数据存储结构,提出联合坐标的表达方式;其次,利用空间积分回归的方法来计算卷积网络的输出结果;最后将输出送入全连接网络进行联合训练.所提方法在human3.6m数据集的2种标准测试协议下进行了测试,取得了比以往大部分方法更高的精确度,面对MPI-INF-3DHP等数据集时也展现出良好的泛化能力.

     

    Abstract: Estimating three-dimensional human pose from color images of single person is a fundamental problem in many applications.However,the problems of inaccuracy and ill-posed poses have been not well solved.A novel deep learning based approach for estimating 3D human poses from images is proposed.First voxel representation is adopted and the joint coordinates are presented to represent the poses.Second,space integral regression is used to compute the output results of the convolutional network.Finally,the output is sent into the fully connected network for joint training.Proposed algorithm has been tested under two standard test protocol of human3.6m dataset.Experimental results show that it obtains higher accuracy than most of previous methods and achieves well generalization ability in MPI-INF-3DHP dataset.

     

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