From TechCrunch
A new paper published by Disney Research in partnership with ETH Zurich describes a fully automated, neural network-based method for swapping faces in photos and videos — the first such method that results in high-resolution, megapixel resolution final results according, to the researchers. That could make it suited for use in film and TV, where high-resolution results are key to ensuring that the final product is good enough to reliably convince viewers as to their reality.
The researchers specifically intend this tech for use in replacing an existing actor's performance with a substitute actor's face, for instance when de-aging or increasing the age of someone, or potentially when portraying an actor who has passed away. They also suggest it could be used for replacing the faces of stunt doubles in cases where the conditions of a scene call for them to be used.
The mouse has the paper
(Score: 0) by Anonymous Coward on Thursday July 02 2020, @02:04PM (3 children)
They're not the same technique.
(Score: 2) by c0lo on Thursday July 02 2020, @02:38PM (1 child)
The technique is the same, tho'. Note the "and software limitations of the DeepFaceLab implementation".
https://www.youtube.com/watch?v=aoFiw2jMy-0 https://soylentnews.org/~MichaelDavidCrawford
(Score: 0) by Anonymous Coward on Thursday July 02 2020, @06:27PM
DeepFakes and DeepFaceLab use Y-shaped autoencoder architectures with Poisson blending, the technique in this paper uses trained Morphable Models with compositing derived from course Laplacian pyramid values in conjunction with contrast correction.
(Score: 0) by Anonymous Coward on Thursday July 02 2020, @03:40PM
If you train an AI to match the results of another AI... is it still the same AI?