Face2Face: Real-time Face Capture and Reenactment of RGB Videos

From Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt and Matthias Nießner:

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... (full paper)

 

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Boston Dynamics Webinar - Why Humanoids Are the Future of Manufacturing

Boston Dynamics Webinar - Why Humanoids Are the Future of Manufacturing

Join us November 18th for this Webinar as we reflect on what we've learned by observing factory floors, and why we've grown convinced that chasing generalization in manipulation—both in hardware and behavior—isn't just interesting, but necessary. We'll discuss AI research threads we're exploring at Boston Dynamics to push this mission forward, and highlight opportunities our field should collectively invest more in to turn the humanoid vision, and the reinvention of manufacturing, into a practical, economically viable product.