论文标题
HEAD2HEAD：基于视频的神经头综合
Head2Head: Video-based Neural Head Synthesis
论文作者
论文摘要
在本文中，我们提出了一种新型的机器学习体系结构，以进行面部重演。特别是，与使用深层卷积神经网络（DCNN）生成单个框架的基于模型的方法或最新的基于框架的方法相反，我们提出了一种新颖的方法，该方法（a）（a）利用面部运动的特殊结构（对口腔运动特别注意）和（b）强制暂时一致性。我们证明，所提出的方法可以将源演员的面部表情，姿势和目光转移到目标视频中，以光真逼真的方式比最先进的方法更准确。
In this paper, we propose a novel machine learning architecture for facial reenactment. In particular, contrary to the model-based approaches or recent frame-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames, we propose a novel method that (a) exploits the special structure of facial motion (paying particular attention to mouth motion) and (b) enforces temporal consistency. We demonstrate that the proposed method can transfer facial expressions, pose and gaze of a source actor to a target video in a photo-realistic fashion more accurately than state-of-the-art methods.
