Recently, image-to-image translation has received increasing attention, which aims to map images in one domain to another specific one. Existing methods mainly solve this task via a deep generative model that they focus on exploring the bi-directional or multi-directional relationship between specific domains. Those domains are often categorized by attribute-level or class-level labels, which do not incorporate any geometric information in learning process. As a result, existing methods are incapable of editing geometric contents during translation. They also neglect to utilize higher-level and instance-specific information to further guide the training process, leading to a great deal of unrealistic synthesized images of low fidelity, especially for face images. To address these challenges, we formulate the general image translation problem as multi-domain mappings in both geometric and attribute directions within an image set that shares a same latent vector. Particularly, we propose a novel Geometrically Editable Generative Adversarial Networks (GEGAN) model to solve this problem for face images by leveraging facial semantic segmentation to explicitly guide its geometric editing. In details, input face images are encoded to their latent representations via a variational autoencoder, a segmentor network is designed to impose semantic information on the generated images, and multi-scale regional discriminators are employed to force the generator to pay attention to the details of key components. We provide both quantitative and qualitative evaluations on CelebA dataset to demonstrate our ability of the geometric modification and our improvement in image fidelity.

中文翻译：

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对抗网络的几何可编辑人脸图像翻译

近来，图像到图像的翻译越来越受到关注，其目的是将一个域中的图像映射到另一特定域。现有方法主要通过一个深层的生成模型来解决此任务，该模型专注于探索特定域之间的双向或多向关系。这些领域通常按属性级别或类级别的标签进行分类，在学习过程中不会包含任何几何信息。结果，现有方法无法在翻译期间编辑几何内容。他们还忽略了利用高级信息和特定于实例的信息来进一步指导训练过程，从而导致大量不真实的低保真合成图像，尤其是面部图像。为了应对这些挑战，我们将一般的图像转换问题公式化为在共享相同潜矢量的图像集中，在几何方向和属性方向上都进行多域映射。特别是，我们提出了一种新颖的几何可编辑生成对抗网络（GEGAN）模型，以通过利用面部语义分割来明确指导其几何编辑来解决面部图像的此问题。具体来说，输入人脸图像通过变体自动编码器被编码为它们的潜在表示，分割器网络被设计为在生成的图像上施加语义信息，并且采用多尺度区域识别器来强制生成器注意图像的细节。关键零件。