Journal of South China University of Technology(Natural Science) >

2024 , Vol. 52 >Issue 7: 19 - 28

DOI: https://doi.org/10.12141/j.issn.1000-565X.230608

Electronics, Communication & Automation Technology

Parametric Human Body Mesh Reconstruction Based on Global Consistency Network

  • BAO Wenxia ,
  • TIAN Ruzhen ,
  • WANG Nian ,
  • CHEN Hemu ,
  • YANG Xianjun
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  • 1.School of Electronic and Information Engineering，Anhui University，Hefei 230601，Anhui，China
    2.The First Affiliated Hospital of Anhui Medical University，Hefei 230022，Anhui，China
    3.Hefei Institute of Physical Sciences，Chinese Academy of Sciences，Hefei 230031，Anhui，China
鲍文霞（1980—），女，教授，博士生导师，主要从事机器学习、图像视频处理和模式识别等研究。Email:bwxia@ahu.edu.cn
王年（1966—），男，教授，博士生导师，主要从事模式识别、计算机视觉、图像处理、智能信息处理等研究。

Received date: 2023-09-28

  Online published: 2024-01-31

Supported by

the National Key Research and Development Program of China(2020YFF0303803);the Key Research and Development Program of Anhui Province(2022k07020006);the Natural Science Research Funding Project of Anhui Universities(KJ2021ZD0004)

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Abstract

Human body mesh reconstruction (HMR) has wide applications in human-computer interaction, virtual/augmented reality, and other fields. In order to further improve the accuracy of human body pose and shape estimation in image-based human body mesh reconstruction, this study proposed a parametric human body mesh reconstruction network based on hybrid inverse kinematics and global consistency deep convolutional neural network, called GloCoNet. To enhance the network’s global consistency and long-range dependencies, a Global Consistency Booster (GCB) module was designed on top of the feature extraction network. It can enhance the model’s perception and expression capabilities of global information, and allow the model to adaptively adjust the feature map weights of different channels and spatial positions. Furthermore, a multi-head attention mechanism was introduced to capture the model’s long-range dependencies globally, helping the model better capture key relationships and patterns when dealing with long-term dependencies, and modeling global contextual information to enrich the diversity of feature subspaces. Meanwhile, the network adopts a hybrid inverse kinematics approach to bridge the gap between human body mesh estimation and 3D human joint estimation, ultimately improving the accuracy of human 3D pose and shape estimation. Experimental results show that the GloCoNet model significantly outperforms previous mainstream methods with an average per joint position error of 51.3 mm on the publicly available Human3.6M dataset.

Key words： human body mesh reconstruction; global consistency; hybrid inverse kinematics; human body parameter

Cite this article

BAO Wenxia , TIAN Ruzhen , WANG Nian , CHEN Hemu , YANG Xianjun . Parametric Human Body Mesh Reconstruction Based on Global Consistency Network[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(7) : 19 -28 . DOI: 10.12141/j.issn.1000-565X.230608

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