  Over the past few years, the automatic generation of facial animation for
virtual characters has garnered interest among the animation research and
industry communities. Recent research contributions leverage machine-learning
approaches to enable impressive capabilities at generating plausible facial
animation from audio and/or video signals. However, these approaches do not
address the problem of animation edition, meaning the need for correcting an
unsatisfactory baseline animation or modifying the animation content itself. In
facial animation pipelines, the process of editing an existing animation is
just as important and time-consuming as producing a baseline. In this work, we
propose a new learning-based approach to easily edit a facial animation from a
set of intuitive control parameters. To cope with high-frequency components in
facial movements and preserve a temporal coherency in the animation, we use a
resolution-preserving fully convolutional neural network that maps control
parameters to blendshapes coefficients sequences. We stack an additional
resolution-preserving animation autoencoder after the regressor to ensure that
the system outputs natural-looking animation. The proposed system is robust and
can handle coarse, exaggerated edits from non-specialist users. It also retains
the high-frequency motion of the facial animation.
