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Face2Face

swMATH ID: 42555
Software Authors: Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt, Matthias Nießner
Description: Face2Face: Real-time Face Capture and Reenactment of RGB Videos. We present Face2Face, 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.
Homepage: https://arxiv.org/abs/2007.14808
Related Software: PyTorch; EfficientNet; Inception-v4; ImageNet; StarGAN; Adam; Xception; PWC-Net; LoFTR; UCF101; TransGAN; InfoGAN; DensePose; ArcFace; Caffe; U-Net; GitHub; OpenGL; MOTS; StyleGAN2
Cited in: 5 Documents

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