论文标题
预验证的面部发电机的自然语言界面的无文本学习
Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
论文作者
论文摘要
我们提出了快速文本2stylegan，这是一种自然语言界面，可适应预先训练的甘恩，以供文本引导的人脸合成。利用对比性语言图像预训练（剪辑）的最新进展，在培训过程中不需要文本数据。 Fast Text2Stylegan被配制为有条件的变异自动编码器（CVAE），可在测试时为生成的图像提供额外的控制和多样性。我们的模型在遇到新的文本提示时不需要重新训练或微调gan或剪辑。与先前的工作相反，我们不依赖于测试时间的优化，这使我们的方法顺序比先前的工作更快。从经验上讲，在FFHQ数据集上，我们的方法提供了与先前的工作相比，自然语言描述中具有不同详细程度的自然语言描述中的图像。
We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis. Leveraging the recent advances in Contrastive Language-Image Pre-training (CLIP), no text data is required during training. Fast text2StyleGAN is formulated as a conditional variational autoencoder (CVAE) that provides extra control and diversity to the generated images at test time. Our model does not require re-training or fine-tuning of the GANs or CLIP when encountering new text prompts. In contrast to prior work, we do not rely on optimization at test time, making our method orders of magnitude faster than prior work. Empirically, on FFHQ dataset, our method offers faster and more accurate generation of images from natural language descriptions with varying levels of detail compared to prior work.
