A Robust Interactive Facial Animation Editing System
Elo\"ise Berson, Catherine Soladi\'e, Vincent Barrielle, Nicolas, Stoiber

TL;DR
This paper introduces a robust, learning-based facial animation editing system that allows intuitive control and preserves natural, high-frequency facial movements, addressing a key gap in existing animation pipelines.
Contribution
The authors develop a novel resolution-preserving neural network and autoencoder framework for easy, natural-looking facial animation editing from control parameters.
Findings
Handles coarse, exaggerated edits effectively
Preserves high-frequency facial motion details
Produces natural-looking animations
Abstract
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…
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