  High quality facial image editing is a challenging problem in the movie
post-production industry, requiring a high degree of control and identity
preservation. Previous works that attempt to tackle this problem may suffer
from the entanglement of facial attributes and the loss of the person's
identity. Furthermore, many algorithms are limited to a certain task. To tackle
these limitations, we propose to edit facial attributes via the latent space of
a StyleGAN generator, by training a dedicated latent transformation network and
incorporating explicit disentanglement and identity preservation terms in the
loss function. We further introduce a pipeline to generalize our face editing
to videos. Our model achieves a disentangled, controllable, and
identity-preserving facial attribute editing, even in the challenging case of
real (i.e., non-synthetic) images and videos. We conduct extensive experiments
on image and video datasets and show that our model outperforms other
state-of-the-art methods in visual quality and quantitative evaluation. Source
codes are available at https://github.com/InterDigitalInc/latent-transformer.
