Inverse mapping of face GANs

Inverse mapping of face GANs

Generative adversarial networks (GANs) synthesize realistic images from a random latent vector. While many studies have explored various training configurations and architectures for GANs, the problem of inverting a generative model to extract latent vectors of given input images has been inadequately investigated.Although there is exactly one generated image per given random vector, the mapping from an image to its recovered latent vector can have more than one solution. We train a ResNet architecture to recover a latent vector for a given face that can be used to generate a face nearly identical to the target. We use a perceptual loss to embed face details in the recovered latent vector while maintaining visual quality using a pixel loss. The vast majority of studies on latent vector recovery perform well only on generated images, we argue that our method can be used to determine a mapping between real human faces and latent-space vectors that contain most of the important face style details. In addition, our proposed method projects generated faces to their latent-space with high fidelity and speed. At last, we demonstrate the performance of our approach on both real and generated faces.

人脸GAN的逆映射

生成对抗网络（GAN）从随机潜在向量合成逼真的图像。尽管许多研究探索了GAN的各种训练配置和体系结构，但对生成模型的反演以提取给定输入图像的潜在向量的问题尚未得到充分研究。.. 尽管每个给定的随机向量只有一个生成的图像，但是从图像到其恢复的潜在向量的映射可以有多个解决方案。我们训练ResNet架构来恢复给定脸部的潜在向量，该潜在向量可用于生成与目标几乎相同的脸部。我们使用感知损失将面部细节嵌入恢复的潜在向量中，同时使用像素损失来保持视觉质量。关于潜矢量恢复的绝大多数研究仅在生成的图像上表现良好，我们认为我们的方法可用于确定真实人脸与包含大多数重要面部样式细节的潜空间矢量之间的映射。此外，我们提出的方法项目以高保真度和速度为其潜在空间生成了面孔。最后， （阅读更多）