Modern image generative models show remarkable sample quality when trained on a single domain or class of objects. In this work, we introduce a generative adversarial network that can simultaneously generate aligned image samples from multiple related domains. We leverage the fact that a variety of object classes share common attributes, with certain geometric differences. We propose Polymorphic-GAN which learns shared features across all domains and a per-domain morph layer to morph shared features according to each domain. In contrast to previous works, our framework allows simultaneous modelling of images with highly varying geometries, such as images of human faces, painted and artistic faces, as well as multiple different animal faces. We demonstrate that our model produces aligned samples for all domains and show how it can be used for applications such as segmentation transfer and cross-domain image editing, as well as training in low-data regimes. Additionally, we apply our Polymorphic-GAN on image-to-image translation tasks and show that we can greatly surpass previous approaches in cases where the geometric differences between domains are large.

本论文提出了一种多样性生成对抗网络模型Polymorphic-GAN，该模型能够同时生成多个相关域中的图像，它具有学习所有域上共享特征和每个域的特征之间的映射关系，能够用于多个应用，比如图像分割传输、跨域图像编辑和低数据域的训练。此外，本文还应用Polymorphic-GAN模型进行了图像到图像的转换任务，实验表明在各种不同几何变化的域中，该模型的性能均大大优于先前的方法。

Polymorphic-GAN: 使用学习到的形态映射在多个域中生成对齐样本