基于时空卷积GAN的面部表情视频生成：FEV-GAN
Facial Expression Video Generation Based-On Spatio-temporal Convolutional GAN: FEV-GAN
AI总结：
提出FEV-GAN，基于时空卷积GAN，采用双图像编码器提取身份和空间特征，生成六种基本面部表情视频，有效提升图像质量和身份保持能力。
摘要：
面部表情生成一直是一项引人入胜的任务。在此背景下，我们提出了一种新方法，用于生成六种基本面部表情的视频。从一张中性面部图像和指示所需面部表情的标签开始，我们旨在合成一个给定身份执行指定面部表情的视频。我们的方法称为FEV-GAN（Facial Expression Video GAN），基于时空卷积GAN，该方法能够在同一网络中建模内容和运动。基于此类网络的先前方法已显示出能够生成具有平滑时间演变的连贯视频的能力。然而，它们仍然存在图像质量低和身份保持能力低的问题。在这项工作中，我们通过使用由两个图像编码器组成的生成器来解决这个问题。第一个预训练用于面部身份特征提取，第二个用于空间特征提取。我们在两个国际面部表情基准数据库：MUG和Oulu-CASIA NIR&VIS上，对我们的模型进行了定性和定量评估。实验结果分析证明了我们的方法在生成六种基本面部表情视频的同时保持输入身份的有效性。分析还证明，同时使用身份和空间特征增强了解码器更好地保持身份和生成高质量视频的能力。代码和预训练模型将很快公开提供。
英文摘要：
Facial expression generation has always been an intriguing task for scientists and researchers all over the globe. In this context, we present our novel approach for generating videos of the six basic facial expressions. Starting from a single neutral facial image and a label indicating the desired facial expression, we aim to synthesize a video of the given identity performing the specified facial expression. Our approach, referred to as FEV-GAN (Facial Expression Video GAN), is based on Spatio-temporal Convolutional GANs, that are known to model both content and motion in the same network. Previous methods based on such a network have shown a good ability to generate coherent videos with smooth temporal evolution. However, they still suffer from low image quality and low identity preservation capability. In this work, we address this problem by using a generator composed of two image encoders. The first one is pre-trained for facial identity feature extraction and the second for spatial feature extraction. We have qualitatively and quantitatively evaluated our model on two international facial expression benchmark databases: MUG and Oulu-CASIA NIR&VIS. The experimental results analysis demonstrates the effectiveness of our approach in generating videos of the six basic facial expressions while preserving the input identity. The analysis also proves that the use of both identity and spatial features enhances the decoder ability to better preserve the identity and generate high-quality videos. The code and the pre-trained model will soon be made publicly available.
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