In recent years, Generative Adversarial Networks (GANs) have improved
steadily towards generating increasingly impressive real-world images. It is
useful to steer the image generation process for purposes such as content
creation. This can be done by conditioning the model on additional information.
However, when conditioning on additional information, there still exists a
large set of images that agree with a particular conditioning. This makes it
unlikely that the generated image is exactly as envisioned by a user, which is
problematic for practical content creation scenarios such as generating facial
composites or stock photos. To solve this problem, we propose a single pipeline
for text-to-image generation and manipulation. In the first part of our
pipeline we introduce textStyleGAN, a model that is conditioned on text. In the
second part of our pipeline we make use of the pre-trained weights of
textStyleGAN to perform semantic facial image manipulation. The approach works
by finding semantic directions in latent space. We show that this method can be
used to manipulate facial images for a wide range of attributes. Finally, we
introduce the CelebTD-HQ dataset, an extension to CelebA-HQ, consisting of
faces and corresponding textual descriptions.

提出了一种单一的文本到图像生成和操纵的流程，其中在我们的流程的第一部分，介绍了 TextStyleGAN 这个在文本上进行训练的模型；第二部分使用预训练的 TextStyleGAN 权重进行语义面部图像操纵，并通过在潜空间中找到语义方向来完成。我们展示了该方法可以用于广泛的面部图像属性操纵，并介绍了 CelebTD-HQ 数据集作为 CelebA-HQ 的扩展，其中包含了人脸及相应的文本描述。