论文标题
婴儿网络：从未校准的照片中重建婴儿的3D面孔
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs
论文作者
论文摘要
我们提出了一个3D面部重建系统，旨在从未校准的照片BabyNet中恢复婴儿的3D面部几何形状。由于婴儿的3D面部几何形状与成年人的面部几何不同，因此需要针对婴儿的面部重建系统。 BabyNet由两个阶段组成：1）3D图卷积自动编码器学习婴儿3D面部形状的潜在空间； 2）一个2D编码器，该编码器根据使用转移学习提取的代表性特征将照片映射到3D潜在空间。这样，使用预训练的3D解码器，我们可以从2D图像中恢复3D面。我们评估婴儿网络并表明1）基于成人数据集的方法无法对婴儿的3D面部几何形状进行建模，这证明了对婴儿特异性方法的需求，而2）即使使用BabyFM等婴儿的3D可变形模型，BabyNet也优于经典模型拟合方法。
We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D facial geometry of babies differs substantially from that of adults, baby-specific facial reconstruction systems are needed. BabyNet consists of two stages: 1) a 3D graph convolutional autoencoder learns a latent space of the baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D latent space based on representative features extracted using transfer learning. In this way, using the pre-trained 3D decoder, we can recover a 3D face from 2D images. We evaluate BabyNet and show that 1) methods based on adult datasets cannot model the 3D facial geometry of babies, which proves the need for a baby-specific method, and 2) BabyNet outperforms classical model-fitting methods even when a baby-specific 3D morphable model, such as BabyFM, is used.
