基于多约束扩张卷积的 3D 人体姿态估计方法

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-08-14 DOI:10.1007/s00530-024-01441-6
Huaijun Wang, Bingqian Bai, Junhuai Li, Hui Ke, Wei Xiang
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引用次数: 0

摘要

近年来,从二维到三维人体姿态估计方法的研究越来越受到关注。然而,这些方法仍需解决深度模糊和自闭塞等问题。为了解决这些问题,我们提出了一种基于多约束扩张卷积的三维人体姿态估计方法。这种方法包括使用基于图卷积的局部约束和基于全连接网络的全局约束。它还利用扩张时间卷积网络来捕捉人体姿势的长期时间相关性。局部约束模块以二维关节坐标序列为输入,构建人体骨骼的交叉关节和等电位连接。全局约束模块对姿势的全局语义信息进行编码。最后,约束模块和人体姿势的时间相关性交替连接,实现三维人体姿势估计。该方法在公共数据集 Human3.6M 和 MPI-INF-3DHP 上进行了验证,结果表明所提出的方法有效地减少了三维人体姿态估计的误差,并表现出一定的泛化能力。
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3D human pose estimation method based on multi-constrained dilated convolutions

In recent years, research on 2D to 3D human pose estimation methods has gained increasing attention. However, these methods, such as depth ambiguity and self-occlusion, still need to be addressed. To address these problems, we propose a 3D human pose estimation method based on multi-constrained dilated convolutions. This approach involves using a local constraint based on graph convolution and a global constraint based on a fully connected network. It also utilizes a dilated temporal convolution network to capture long-term temporal correlations of human poses. Taking 2D joint coordinate sequences as input, the local constraint module constructs cross-joint and equipotential connections for the human skeleton. The global constraint module encodes global semantic information about posture. Finally, the constraint modules and the temporal correlation of human posture are alternately connected to achieve 3D human posture estimation. The method was validated on the public datasets Human3.6M and MPI-INF-3DHP, and the results show that the proposed method effectively reduces the error in 3D human pose estimation and demonstrates a certain degree of generalization ability.

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CiteScore
7.20
自引率
4.30%
发文量
567
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