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Deep Video Portraits


Kim,  Hyeongwoo
Computer Graphics, MPI for Informatics, Max Planck Society;


Tewari,  Ayush
Computer Graphics, MPI for Informatics, Max Planck Society;


Xu,  Weipeng
Computer Graphics, MPI for Informatics, Max Planck Society;


Theobalt,  Christian       
Computer Graphics, MPI for Informatics, Max Planck Society;

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Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Nießner, M., et al. (2018). Deep Video Portraits. Retrieved from http://arxiv.org/abs/1805.11714.

Cite as: https://hdl.handle.net/21.11116/0000-0002-5E1E-5
We present a novel approach that enables photo-realistic re-animation of
portrait videos using only an input video. In contrast to existing approaches
that are restricted to manipulations of facial expressions only, we are the
first to transfer the full 3D head position, head rotation, face expression,
eye gaze, and eye blinking from a source actor to a portrait video of a target
actor. The core of our approach is a generative neural network with a novel
space-time architecture. The network takes as input synthetic renderings of a
parametric face model, based on which it predicts photo-realistic video frames
for a given target actor. The realism in this rendering-to-video transfer is
achieved by careful adversarial training, and as a result, we can create
modified target videos that mimic the behavior of the synthetically-created
input. In order to enable source-to-target video re-animation, we render a
synthetic target video with the reconstructed head animation parameters from a
source video, and feed it into the trained network -- thus taking full control
of the target. With the ability to freely recombine source and target
parameters, we are able to demonstrate a large variety of video rewrite
applications without explicitly modeling hair, body or background. For
instance, we can reenact the full head using interactive user-controlled
editing, and realize high-fidelity visual dubbing. To demonstrate the high
quality of our output, we conduct an extensive series of experiments and
evaluations, where for instance a user study shows that our video edits are
hard to detect.