![]() New technologies like Virtual Reality or Augmented Reality rely on such data. The reconstruction of the surrounding is very important nowadays. Computer Vision tries to obtain this model from image data. Such a model enables computers to reconstruct, understand, and interact with it. Our primary goal is to create a mathematical model of our world. Hopefully, you enjoy watching our video, and we hope to provide a positive takeaway :) Please also note that our efforts include the detection of edits in video footage in order to verify a clip's authenticity. The novelty and contribution of our work is that we can edit pre-recorded videos in real-time on a commodity PC. These results are hard to distinguish from reality and it often goes unnoticed that the content is not real. Virtually every high-end movie production contains a significant percentage of synthetically-generated content (from Lord of the Rings to Benjamin Button). We want to emphasize that computer-generated videos have been part in feature-film movies for over 30 years. Our aim is to demonstrate the capabilities of modern computer vision and graphics technology, and convey it in an approachable and fun way. This demo video is purely research-focused and we would like to clarify the goals and intent of our work. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time. 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. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Reenactment is then achieved by fast and efficient deformation transfer between source and target. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. ![]() 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. The source sequence is also a monocular video stream, captured live with a commodity webcam. We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). Face2Face: Real-time Face Capture and Reenactment of RGB Videos
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