Creating realistic animations of human faces is still a challenging task in computer graphics. While computer graphics (CG) models capture much variability in a small parameter vector, they usually do not meet the necessary visual quality. This is due to the fact, that geometry-based animation often does not allow fine-grained deformations and fails in difficult areas (mouth, eyes) to produce realistic renderings. Image-based animation techniques avoid these problems by using dynamic textures that capture details and small movements that are not explained by geometry. This comes at the cost of high-memory requirements and limited flexibility in terms of animation because dynamic texture sequences need to be concatenated seamlessly, which is not always possible and prone to visual artefacts. In this study, the authors present a new hybrid animation framework that exploits recent advances in deep learning to provide an interactive animation engine that can be used via a simple and intuitive visualisation for facial expression editing. The authors describe an automatic pipeline to generate training sequences that consist of dynamic textures plus sequences of consistent three-dimensional face models. Based on this data, they train a variational autoencoder to learn a low-dimensional latent space of facial expressions that is used for interactive facial animation.

中文翻译：

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具有深度神经网络的交互式面部动画

在计算机图形学中，创建逼真的人脸动画仍然是一项艰巨的任务。尽管计算机图形（CG）模型在小的参数向量中捕获了很大的可变性，但它们通常不符合必要的视觉质量。这是由于以下事实：基于几何的动画通常不允许细粒度的变形，并且在困难的区域（嘴，眼睛）无法产生逼真的效果。基于图像的动画技术通过使用动态纹理来捕获这些细节，而动态纹理可以捕获几何图形无法解释的细节和微小运动，从而避免了这些问题。这是以高内存需求和动画方面有限的灵活性为代价的，因为动态纹理序列需要无缝连接，这并不总是可能的，并且容易出现视觉伪像。在这个研究中，作者提出了一个新的混合动画框架，该框架利用深度学习的最新进展提供了一个交互式动画引擎，可以通过简单直观的可视化将其用于面部表情编辑。作者描述了一种自动流水线，用于生成训练序列，该序列由动态纹理加上一致的三维人脸模型序列组成。基于这些数据，他们训练了变分自动编码器，以学习用于交互式面部动画的面部表情的低维潜在空间。作者描述了一种自动流水线，用于生成训练序列，该序列由动态纹理加上一致的三维人脸模型序列组成。基于这些数据，他们训练了变分自动编码器，以学习用于交互式面部动画的面部表情的低维潜在空间。作者描述了一种自动流水线，用于生成训练序列，该序列由动态纹理加上一致的三维人脸模型序列组成。基于这些数据，他们训练了变分自动编码器，以学习用于交互式面部动画的面部表情的低维潜在空间。