Multi-view face generation from a single image is an essential and challenging problem. Most of the existing methods need to use paired images when training models. However, collecting and labeling large-scale paired face images could lead to high labor and time cost. In order to address this problem, multi-view face generation via unpaired images is proposed in this paper. To avoid using paired data, the encoder and discriminator are trained, so that the high-level abstract features of the identity and view of the input image are learned by the encoder, and then, these low-dimensional data are input into the generator, so that the realistic face image can be reconstructed by the training generator and discriminator. During testing, multiple one-hot vectors representing the view are imposed to the identity representation and the generator is employed to map them to high-dimensional data, respectively, which can generate multi-view images while preserving the identity features. Furthermore, to reduce the number of used labels, semi-supervised learning is used in the model. The experimental results show that our method can produce photo-realistic multi-view face images with a small number of view labels, and makes a useful exploration for the synthesis of face images via unpaired data and very few labels.

从单个图像生成多视图面部是一个必不可少且具有挑战性的问题。训练模型时，大多数现有方法都需要使用配对图像。然而，收集和标记大规模的成对面部图像可能导致高人工和时间成本。为了解决这个问题，本文提出了通过不成对图像生成多视角人脸的方法。为避免使用成对的数据，对编码器和鉴别器进行了训练，以便编码器学习输入图像的身份和视图的高级抽象特征，然后将这些低维数据输入到生成器中，从而可以通过训练生成器和判别器重建真实的人脸图像。在测试期间，将代表视图的多个单热点向量应用于身份表示，并使用生成器将它们分别映射到高维数据，这些数据可以在保留身份特征的同时生成多视图图像。此外，为了减少使用的标签数量，在模型中使用了半监督学习。实验结果表明，该方法可以生成带有少量视图标签的逼真的多视图人脸图像，为通过不成对数据和很少标签进行人脸图像合成提供了有益的探索。