Expressions from the source are transferred from source to target actor, while retaining the head pose (rotation and translation) as well as the eye gaze of the target actor On top of that, full video of the target is synthesized, including consistent upper body posture, hair, and background.įigure 1.
The core method presented in the paper provides full control over the head of a target actor by transferring the rigid head pose, facial expressions, and eye motion of a _source actor, _while preserving the target’s identity and appearance. The word DeepFake combines the terms “deep learning” and “fake”, and refers to manipulated videos or other digital representations that produce fabricated images and sounds that appear to be real but have in fact been generated by deep neural networks. Second, an overview of the paper “Deep Video Portraits” in the words of the authors. In this post, I’ll cover two things: First, a short definition of a DeepFake. In this post, I’m going to review “Deep Video Portraits”, which presents a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. There hasn’t been an approach that enables one to take full control of the rigid head pose, face expressions, and eye motion of the target actor even face identity can be modified to some extent. The problem of synthesizing a photo-realistic video portrait of a target actor that mimics the actions of a source actor-and especially where the source and target actors can be different subjects-is still an open problem.
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Synthesizing and editing video portraits-i.e., videos framed to show a person’s head and upper body-is an important problem in computer graphics, with applications in video editing and movie postproduction, visual effects, visual dubbing, virtual reality, and telepresence, among others.
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