In the field of 3D face alignment, most researchers have focused on improving the prediction accuracy of algorithms and ignored the portability for practical applications. To this end, this study presents a real-time 3D face-alignment method that uses an encoder-decoder network with an efficient deconvolution layer. The fusion of the encoding and decoding feature adds more abundant features to this network. An efficient deconvolution layer at the decoding stage applies the L1 norm to select useful features and generate abundant ones through linear operations. Experimental results using the standard AFLW2000-3D and AFLW-LFPA datasets show that our algorithm has low prediction errors with real-time applicability.

中文翻译：

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使用具有高效反卷积层的编码器-解码器网络进行实时 3D 人脸对齐

在 3D 人脸对齐领域，大多数研究人员都专注于提高算法的预测精度，而忽略了实际应用的可移植性。为此，本研究提出了一种实时 3D 面部对齐方法，该方法使用具有高效反卷积层的编码器-解码器网络。编码和解码特征的融合为这个网络增加了更丰富的特征。解码阶段的高效反卷积层应用 L1 范数来选择有用的特征并通过线性运算生成丰富的特征。使用标准 AFLW2000-3D 和 AFLW-LFPA 数据集的实验结果表明，我们的算法具有低预测误差和实时适用性。