Fine-grained facial expression aims at changing the expression of an image without altering facial identity. Most current expression manipulation methods are based on a discrete expression label, which mainly manipulates holistic expression with details neglected. To handle the above mentioned problems, we propose an end-to-end expression-guided generative adversarial network (EGGAN), which synthesizes an image with expected expression given continuous expression label and structured latent code. In particular, an adversarial autoencoder is used to translate a source image into a structured latent space. The encoded latent code and the target expression label are input to a conditional GAN to synthesize an image with the target expression. Moreover, a perceptual loss and a multiscale structural similarity loss are introduced to preserve facial identity and global shape during expression manipulation. Extensive experiments demonstrate that our approach can edit fine-grained expressions, and synthesize continuous intermediate expressions between source and target expressions.

中文翻译：

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EGGAN：学习细粒度表达式操作的潜在空间

细粒度面部表情旨在在不改变面部身份的情况下改变图像的表情。目前大多数表情操作方法都是基于离散的表情标签，主要是对整体表情进行操作，忽略了细节。为了解决上述问题，我们提出了一种端到端的表达引导生成对抗网络（EGGAN），它通过给定连续表达标签和结构化潜在代码来合成具有预期表达的图像。特别是，对抗性自动编码器用于将源图像转换为结构化的潜在空间。编码后的潜在代码和目标表达标签被输入到条件 GAN 以合成具有目标表达的图像。而且，引入了感知损失和多尺度结构相似性损失，以在表情操作期间保持面部特征和全局形状。大量实验表明，我们的方法可以编辑细粒度表达式，并在源表达式和目标表达式之间合成连续的中间表达式。