  For the last decades, the concern of producing convincing facial animation
has garnered great interest, that has only been accelerating with the recent
explosion of 3D content in both entertainment and professional activities. The
use of motion capture and retargeting has arguably become the dominant solution
to address this demand. Yet, despite high level of quality and automation
performance-based animation pipelines still require manual cleaning and editing
to refine raw results, which is a time- and skill-demanding process. In this
paper, we look to leverage machine learning to make facial animation editing
faster and more accessible to non-experts. Inspired by recent image inpainting
methods, we design a generative recurrent neural network that generates
realistic motion into designated segments of an existing facial animation,
optionally following user-provided guiding constraints. Our system handles
different supervised or unsupervised editing scenarios such as motion filling
during occlusions, expression corrections, semantic content modifications, and
noise filtering. We demonstrate the usability of our system on several
animation editing use cases.
