Eerily Realistic Software Allows One To Easily Simulate Another’s Face

This standard use of computer vision and pattern recognition is as Stanford scholar's state a novel approach for real-time facial reenactment of a monocular target video sequence.

The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination

Face2Face shows examples where YouTube videos are taken and reenacted of former President George Bush, Russian President Putin, and even Donald Trump, manipulating every part of their face and words.

They introduce a new method using an online RGB tracking pipeline against state-of-the-art reenactment systems like Cao et al. and Thies et al. They have concluded that although Thies et al. uses RGB data, their own RGB only system has achieved similar face tracking quality, or even better, because Thies et al.'s method leaves the mouth with a slight deformation.

Videos or news of any kind could have already been altered to create false propganda, and many people have probably already watched them while believing every word.




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