I recently ran into that issue - I have a nice fine-tuned yocto image and now a customer wants to "log in via ssh and install a custom raspbian package". I was thinking of the docker solution as well.
Yes, but it just cuts out the face and pastes it on a different person/background. It does not do full reenactment where you keep the entire target video environment.
From the paper:
"we have to use all the GPUs in DGX1 (8 V100 GPUs, each with 16GB memory) for training. We distribute the generator computation task to 4 GPUs and the discriminator computation task to the other 4 GPUs. Training takes ∼10 days for 2K resolution."
As I don't have a DGX1 here, training the 2K resolution net for 10 days on a p3.16xlarge instance (also has 8 V100 GPUs) would cost USD 5875 on AWS.
(USD24.48 per hour on-demand pricing * 24 hours/day * 10 days)
One of the example translates a full human pose to a video of a dancer. If the network would be trained on the facial pose(?) / features only, would that recreate something like the facial reenactment in http://niessnerlab.org/projects/thies2016face.html (source code for face2face is not public)?