Face2Face is a real-time Face capture and reenactment of RGB Videos. We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). 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. We demonstrate our method in a live setup, where YouTube videos are reenacted in real time.
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I must thank you for the efforts youve put in penning this site. I am hoping to check out the same high-grade blog posts by you in the future as well. In fact, your creative writing abilities has motivated me to get my very own blog now 😉
Itís hard to find experienced people about this subject, however, you sound like you know what youíre talking about! Thanks