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.

中文翻译：

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基于生成 RNN 框架的直观面部动画编辑

在过去的几十年里，制作令人信服的面部动画的关注引起了极大的兴趣，随着最近娱乐和专业活动中 3D 内容的爆炸式增长，这种关注只会加速。运动捕捉和重定向的使用可以说已成为满足这一需求的主要解决方案。然而，尽管高质量和自动化的基于性能的动画管道仍然需要手动清理和编辑来优化原始结果，这是一个需要时间和技能的过程。在本文中，我们希望利用机器学习使非专家更快、更容易地进行面部动画编辑。受最近图像修复方法的启发，我们设计了一个生成循环神经网络，可以将逼真的运动生成到现有面部动画的指定片段中，可选地遵循用户提供的指导约束。我们的系统处理不同的有监督或无监督编辑场景，例如遮挡期间的运动填充、表情校正、语义内容修改和噪声过滤。我们在几个动画编辑用例中展示了我们系统的可用性。