You Xingyi, Wang Yue, Zhao Xiaohu
National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology, Xuzhou 221008, China.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China.
Sensors (Basel). 2023 Jul 27;23(15):6713. doi: 10.3390/s23156713.
In the past few years, 3D Morphing Model (3DMM)-based methods have achieved remarkable results in single-image 3D face reconstruction. However, high-fidelity 3D face texture generation has been successfully achieved with this method, which mostly uses the power of deep convolutional neural networks during the parameter fitting process, which leads to an increase in the number of network layers and computational burden of the network model and reduces the computational speed. Currently, existing methods increase computational speed by using lightweight networks for parameter fitting, but at the expense of reconstruction accuracy. In order to solve the above problems, we improved the 3D deformation model and proposed an efficient and lightweight network model: Mobile-FaceRNet. First, we combine depthwise separable convolution and multi-scale representation methods to fit the parameters of a 3D deformable model (3DMM); then, we introduce a residual attention module during network training to enhance the network's attention to important features, guaranteeing high-fidelity facial texture reconstruction quality; and, finally, a new perceptual loss function is designed to better address smoothness and image similarity for the smoothing constraints. Experimental results show that the method proposed in this paper can not only achieve high-precision reconstruction under the premise of lightweight, but it is also more robust to influences such as attitude and occlusion.
在过去几年中，基于三维变形模型（3DMM）的方法在单图像三维人脸重建方面取得了显著成果。然而，使用这种方法成功实现高保真三维人脸纹理生成时，在参数拟合过程中大多借助深度卷积神经网络的能力，这导致网络层数增加和网络模型计算负担加重，降低了计算速度。目前，现有方法通过使用轻量级网络进行参数拟合来提高计算速度，但以牺牲重建精度为代价。为了解决上述问题，我们改进了三维变形模型，提出了一种高效轻量级网络模型：移动人脸回归网络（Mobile - FaceRNet）。首先，我们结合深度可分离卷积和多尺度表示方法来拟合三维可变形模型（3DMM）的参数；然后，在网络训练期间引入残差注意力模块，以增强网络对重要特征的关注，保证高保真面部纹理重建质量；最后，设计了一种新的感知损失函数，以更好地处理平滑约束下的平滑度和图像相似性。实验结果表明，本文提出的方法不仅能在轻量级前提下实现高精度重建，而且对姿态和遮挡等影响更具鲁棒性。
