论文标题
Glassesgan：使用合成外观发现和目标子空间建模的眼镜个性化
GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling
论文作者
论文摘要
我们展示了玻璃杯，这是一种针对玻璃自定义设计的新型图像编辑框架，该框架为图像质量，编辑现实主义和连续的多式编辑功能设定了新标准。为了促进使用玻璃杯的编辑过程，我们提出了针对性的子空间建模（TSM）程序，该程序基于在预训练的GAN生成器的潜在空间（合成）外观发现的新机制，构建了Eyeglasses特异性（潜在的）（潜在的）子空间，该子空间可以利用编辑框架。此外，我们还引入了一个外观约束的子空间初始化（SI）技术，该技术将给定输入图像的潜在表示中心在构造的子空间的定义明确的部分中，以提高学识渊博的编辑的可靠性。我们测试了两个（多样）高分辨率数据集（Celeba-HQ和SiblingsDB-HQF）上的镜头，并将其与三个最先进的竞争对手（即Interfacegan，Ganspace，Ganspace和Maskgan）进行比较。报告的结果表明，眼镜令人信服地胜过所有竞争技术，同时提供了其他竞争对手的其他功能（例如，细粒度的多式编辑）。源代码将免费提供。
We present GlassesGAN, a novel image editing framework for custom design of glasses, that sets a new standard in terms of image quality, edit realism, and continuous multi-style edit capability. To facilitate the editing process with GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that, based on a novel mechanism for (synthetic) appearance discovery in the latent space of a pre-trained GAN generator, constructs an eyeglasses-specific (latent) subspace that the editing framework can utilize. Additionally, we also introduce an appearance-constrained subspace initialization (SI) technique that centers the latent representation of the given input image in the well-defined part of the constructed subspace to improve the reliability of the learned edits. We test GlassesGAN on two (diverse) high-resolution datasets (CelebA-HQ and SiblingsDB-HQf) and compare it to three state-of-the-art competitors, i.e., InterfaceGAN, GANSpace, and MaskGAN. The reported results show that GlassesGAN convincingly outperforms all competing techniques, while offering additional functionality (e.g., fine-grained multi-style editing) not available with any of the competitors. The source code will be made freely available.
