  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.
