论文标题
带有多个源图像的动画
Face Animation with Multiple Source Images
论文作者
论文摘要
近年来，由于其广泛的有希望的应用，Face Animation引起了研究人员的广泛关注。许多基于光流或深度神经网络的面部动画模型取得了巨大的成功。但是，这些模型可能会在动画场景中失败，并具有重大视图变化，从而导致面孔不切实际或扭曲。可能的原因之一是，这样的模型缺乏对人脸的先验知识，并且不熟练想象他们从未见过的面部区域。在本文中，我们提出了一种灵活而通用的方法，可以在没有其他培训的情况下提高面部动画的性能。我们使用多个源图像作为输入，以赔偿缺乏面孔的先验知识。我们的方法的有效性是在实验上证明的，其中提出的方法成功地补充了基线方法。
Face animation has received a lot of attention from researchers in recent years due to its wide range of promising applications. Many face animation models based on optical flow or deep neural networks have achieved great success. However, these models are likely to fail in animated scenarios with significant view changes, resulting in unrealistic or distorted faces. One of the possible reasons is that such models lack prior knowledge of human faces and are not proficient to imagine facial regions they have never seen before. In this paper, we propose a flexible and generic approach to improve the performance of face animation without additional training. We use multiple source images as input as compensation for the lack of prior knowledge of faces. The effectiveness of our method is experimentally demonstrated, where the proposed method successfully supplements the baseline method.
