In this work, we introduce a novel framework based on Generative Adversarial Networks to control the pose, expression and facial features of a given face image using another face image. It can then be used for data augmentation, pose invariant face identification, face verification, and lightweight image editing. Generating new realistic face images with controllable poses, facial features, and expressions is a challenging generative learning problem due to skin tone variations, the identity preservation problem, necessity to deal with unseen large poses, and the absence of ground truth images in the training process. We make the following contributions. First, we present a network, CtrlFaceNet that can control a source face image while preserving the identity and skin tone. Second, we introduce a method for training the framework in fully self-supervised mode using a large-scale dataset of unconstrained face images. Third, we show that the style loss function can be used to preserve the skin tone of the source image. The experimental results show that our approach outperforms all other baselines. Furthermore, to the best of our knowledge, we are the first to train such a model using large-scale dataset of unconstrained face images.

在这项工作中，我们介绍了一个基于生成对抗网络的新颖框架，以使用另一个面部图像控制给定面部图像的姿势，表情和面部特征。然后可以将其用于数据增强，姿势不变的人脸识别，人脸验证和轻量级图像编辑。由于肤色变化，身份保留问题，必须处理看不见的大姿势以及训练过程中没有地面真相图像，生成具有可控姿势，面部特征和表情的新逼真的面部图像是具有挑战性的生成学习问题。 。我们做出以下贡献。首先，我们介绍一个网络CtrlFaceNet，它可以控制源面部图像，同时保留身份和肤色。第二，我们介绍了一种使用大规模无约束人脸图像数据集以完全自我监督模式训练框架的方法。第三，我们表明样式丢失功能可用于保留源图像的肤色。实验结果表明，我们的方法优于所有其他基准。此外，据我们所知，我们是第一个使用无约束人脸图像的大规模数据集训练这种模型的人。