论文标题
用于基于样式的GAN倒置的功能式编码器
Feature-Style Encoder for Style-Based GAN Inversion
论文作者
论文摘要
我们为GAN倒置提出了一种新颖的架构，我们称之为功能式编码器。样式编码器是操纵所获得的潜在代码的关键，而该功能编码器对于最佳图像重建至关重要。我们的模型从基于预训练的基于样式的GAN模型的潜在空间中实现了真实图像的准确反转，比现有方法获得了更好的感知质量和更低的重建误差。由于其编码器结构，该模型允许快速准确的图像编辑。此外，我们证明了所提出的编码器特别适合在视频上进行反转和编辑。我们对在不同数据域进行了预先训练的几种基于样式的发电机进行广泛的实验。我们提出的方法为基于样式的GAN倒置产生最先进的结果，这显着优于竞争方法。源代码可在https://github.com/interdigitalinc/featurestyleencoder上找到。
We propose a novel architecture for GAN inversion, which we call Feature-Style encoder. The style encoder is key for the manipulation of the obtained latent codes, while the feature encoder is crucial for optimal image reconstruction. Our model achieves accurate inversion of real images from the latent space of a pre-trained style-based GAN model, obtaining better perceptual quality and lower reconstruction error than existing methods. Thanks to its encoder structure, the model allows fast and accurate image editing. Additionally, we demonstrate that the proposed encoder is especially well-suited for inversion and editing on videos. We conduct extensive experiments for several style-based generators pre-trained on different data domains. Our proposed method yields state-of-the-art results for style-based GAN inversion, significantly outperforming competing approaches. Source codes are available at https://github.com/InterDigitalInc/FeatureStyleEncoder .
