  Learning-based gaze estimation methods require large amounts of training data
with accurate gaze annotations. Facing such demanding requirements of gaze data
collection and annotation, several image synthesis methods were proposed, which
successfully redirected gaze directions precisely given the assigned
conditions. However, these methods focused on changing gaze directions of the
images that only include eyes or restricted ranges of faces with low resolution
(less than $128\times128$) to largely reduce interference from other attributes
such as hairs, which limits application scenarios. To cope with this
limitation, we proposed a portable network, called ReDirTrans, achieving
latent-to-latent translation for redirecting gaze directions and head
orientations in an interpretable manner. ReDirTrans projects input latent
vectors into aimed-attribute embeddings only and redirects these embeddings
with assigned pitch and yaw values. Then both the initial and edited embeddings
are projected back (deprojected) to the initial latent space as residuals to
modify the input latent vectors by subtraction and addition, representing old
status removal and new status addition. The projection of aimed attributes only
and subtraction-addition operations for status replacement essentially mitigate
impacts on other attributes and the distribution of latent vectors. Thus, by
combining ReDirTrans with a pretrained fixed e4e-StyleGAN pair, we created
ReDirTrans-GAN, which enables accurately redirecting gaze in full-face images
with $1024\times1024$ resolution while preserving other attributes such as
identity, expression, and hairstyle. Furthermore, we presented improvements for
the downstream learning-based gaze estimation task, using redirected samples as
dataset augmentation.

Computer Vision and Pattern Recognition