基于深度三维变形的单张3D人脸重建算法
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郑州职业技术学院软件工程系

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河南省科技厅科技计划项目(162102310240). 河南省高等学校重点科研项目(16A460013)


Single sheet 3D face reconstruction algorithm based on deep 3D Morphable Model
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Department of Software Engineering, Zhengzhou Technical College

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    摘要:

    三维变形模型(3DMM)作为人脸建模的重要方式,在三维建模、图像合成等领域有着广泛的应用。由于受训练数据类型、数量以及线性基等因素影响,3DMM存在过约束的现象,不能提供足够的灵活性来表示高频变形。为了进一步改善这一问题,本文将三维变形基函数嵌入到深度神经网络中,并为提升模型的表示能力提供了新的思路。为了提升网络学习效率,本文构设了一种双通路神经网络,实现了在全局路径和局部路径之间的平衡;通过在学习目标和网络结构两方面改进非线性3DMM,提出了一种比线性或以往的非线性模型更能捕捉到更高层次细节的模型。算法对比与仿真实验表明,该算法在三维人脸建模上的归一化平均误差更低,所生成的三维人脸模型鲁棒性好,重构准确,实现了较好的三维人脸重建性能。

    Abstract:

    As an important method of face modeling, 3D deformation model (3DMM) has been widely used in 3D modeling, image synthesis and other fields. Due to the influence of training data type, quantity, linear basis and other factors, 3DMM has the phenomenon of over-constraint and cannot provide enough flexibility to represent high-frequency deformation. In order to further improve the problem, the three-dimensional deformation basis function is embedded into the deep neu-ral network, which provides a new idea for improving the representation ability of the model. In order to improve the efficiency of network learning, a two-path neural network is constructed to achieve the balance between global path and local path. By improving nonlinear 3DMM in both learning objectives and network structure, we propose a model that can capture higher levels of detail than linear or previous nonlinear models. The algorithm is compared with the simula-tion experiment, which shows that the algorithm has lower normalized average error in 3D face modeling, and the gen-erated 3D face model has good robustness and accurate reconstruction, and achieves better 3D face reconstruction per-formance.

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杜召彬.基于深度三维变形的单张3D人脸重建算法[J].南京信息工程大学学报,,():

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  • 收稿日期:2021-09-25
  • 最后修改日期:2021-10-31
  • 录用日期:2021-11-03
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