Deep Video Portraits


Hyeongwoo Kim1  Pablo Garrido2  Ayush Tewari1  Weipeng Xu1  Justus Thies3 
Matthias Nießner3  Patrick Pérez2  Christian Richardt4  Michael Zollhöfer5  Christian Theobalt1
1MPI Informatics  2Technicolor  3Technical University of Munich  4University of Bath  5Stanford University


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 reanimation, 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 highfidelity 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.


  title     = {Deep Video Portraits},
  author    = {Kim, Hyeongwoo and Garrido, Pablo and Tewari, Ayush and Xu, Weipeng and Thies, Justus and 
               Nie{\ss}ner, Matthias and P{\'e}rez, Patrick and Richardt, Christian and Zoll{\"o}fer, Michael and Theobalt, Christian},
  journal   = {ACM Transactions on Graphics (TOG)},
  volume    = {37},
  number    = {4},
  pages     = {},
  year      = {2018},
  publisher = {ACM}