无需标注，一键实现人脸姿态与表情迁移。
Pose and Facial Expression Transfer by using StyleGAN
- 用双编码器+映射网络将两图嵌入StyleGAN2潜空间
- 视频自监督训练，生成效果逼真且可实时运行
- 适合虚拟角色动画与数字人表情控制
我们提出一种在人脸图像间迁移姿态和表情的方法。给定源图像和目标肖像，模型生成一张新图，其中源脸的姿态与表情被迁移到目标身份上。架构包含两个编码器和一个映射网络，将输入投影到StyleGAN2的潜空间，并最终生成输出图像。训练采用多人视频序列进行自监督，无需人工标注。该模型可合成具有可控姿态与表情的随机身份图像，实现接近实时的性能。
原文摘要 · Abstract (English)
We propose a method to transfer pose and expression between face images. Given a source and target face portrait, the model produces an output image in which the pose and expression of the source face image are transferred onto the target identity. The architecture consists of two encoders and a mapping network that projects the two inputs into the latent space of StyleGAN2, which finally generates the output. The training is self-supervised from video sequences of many individuals. Manual labeling is not required. Our model enables the synthesis of random identities with controllable pose and expression. Close-to-real-time performance is achieved.
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