  The ability of Generative Adversarial Networks to encode rich semantics
within their latent space has been widely adopted for facial image editing.
However, replicating their success with videos has proven challenging. Sets of
high-quality facial videos are lacking, and working with videos introduces a
fundamental barrier to overcome - temporal coherency. We propose that this
barrier is largely artificial. The source video is already temporally coherent,
and deviations from this state arise in part due to careless treatment of
individual components in the editing pipeline. We leverage the natural
alignment of StyleGAN and the tendency of neural networks to learn low
frequency functions, and demonstrate that they provide a strongly consistent
prior. We draw on these insights and propose a framework for semantic editing
of faces in videos, demonstrating significant improvements over the current
state-of-the-art. Our method produces meaningful face manipulations, maintains
a higher degree of temporal consistency, and can be applied to challenging,
high quality, talking head videos which current methods struggle with.
