We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.

The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CELEBA images at 1024 2 . We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CELEBA dataset.

Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.

Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset. We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images. We expect this to open up new application domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42.

We explore and analyze the latent style space of Style-GAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

GAN的进展使高分辨率的感性质量形象产生了产生。 stylegans允许通过数学操作对W/W+空间中的潜在样式向量进行数学操作进行引人入胜的属性修改，从而有效调节生成器的丰富层次结构表示。最近，此类操作已被推广到原始StyleGan纸中的属性交换之外，以包括插值。尽管StyleGans有许多重大改进，但仍被认为会产生不自然的图像。生成的图像的质量基于两个假设。 （a）生成器学到的层次表示的丰富性，以及（b）样式空间的线性和平滑度。在这项工作中，我们提出了一个层次的语义正常化程序（HSR），该层次正常化程序将生成器学到的层次表示与大量数据学到的相应的强大功能保持一致。 HSR不仅可以改善发电机的表示，还可以改善潜在风格空间的线性和平滑度，从而导致产生更自然的样式编辑的图像。为了证明线性改善，我们提出了一种新型的度量 - 属性线性评分（ALS）。通过改善感知路径长度（PPL）度量的改善，在不同的标准数据集中平均16.19％的不自然图像的生成显着降低，同时改善了属性编辑任务中属性变化的线性变化。

We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearing to be glued to image coordinates instead of the surfaces of depicted objects. We trace the root cause to careless signal processing that causes aliasing in the generator network. Interpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process. The resulting networks match the FID of StyleGAN2 but differ dramatically in their internal representations, and they are fully equivariant to translation and rotation even at subpixel scales. Our results pave the way for generative models better suited for video and animation. * This work was done during an internship at NVIDIA. 35th Conference on Neural Information Processing Systems (NeurIPS 2021).

生成的对抗网络（GANS）已经实现了图像生成的照片逼真品质。但是，如何最好地控制图像内容仍然是一个开放的挑战。我们介绍了莱特基照片，这是一个两级GaN，它在古典GAN目标上训练了训练，在一组空间关键点上有内部调节。这些关键点具有相关的外观嵌入，分别控制生成对象的位置和样式及其部件。我们使用合适的网络架构和培训方案地址的一个主要困难在没有领域知识和监督信号的情况下将图像解开到空间和外观因素中。我们展示了莱特基点提供可解释的潜在空间，可用于通过重新定位和交换Keypoint Embedding来重新安排生成的图像，例如通过组合来自不同图像的眼睛，鼻子和嘴巴来产生肖像。此外，关键点和匹配图像的显式生成启用了一种用于无监督的关键点检测的新的GaN的方法。

The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations.

我们通过将此任务视为视觉令牌生成问题来提出新的视角来实现图像综合。与现有的范例不同，即直接从单个输入（例如，潜像）直接合成完整图像，新配方使得能够为不同的图像区域进行灵活的本地操作，这使得可以学习内容感知和细粒度的样式控制用于图像合成。具体地，它需要输入潜像令牌的序列，以预测用于合成图像的视觉令牌。在这种观点来看，我们提出了一个基于令牌的发电机（即Tokengan）。特别是，Tokengan输入了两个语义不同的视觉令牌，即，来自潜在空间的学习常量内容令牌和风格代币。鉴于一系列风格令牌，Tokengan能够通过用变压器将样式分配给内容令牌来控制图像合成。我们进行了广泛的实验，并表明拟议的Tokengan在几个广泛使用的图像综合基准上实现了最先进的结果，包括FFHQ和LSUN教会，具有不同的决议。特别地，发电机能够用1024x1024尺寸合成高保真图像，完全用卷曲分配。

Recent 3D-aware GANs rely on volumetric rendering techniques to disentangle the pose and appearance of objects, de facto generating entire 3D volumes rather than single-view 2D images from a latent code. Complex image editing tasks can be performed in standard 2D-based GANs (e.g., StyleGAN models) as manipulation of latent dimensions. However, to the best of our knowledge, similar properties have only been partially explored for 3D-aware GAN models. This work aims to fill this gap by showing the limitations of existing methods and proposing LatentSwap3D, a model-agnostic approach designed to enable attribute editing in the latent space of pre-trained 3D-aware GANs. We first identify the most relevant dimensions in the latent space of the model controlling the targeted attribute by relying on the feature importance ranking of a random forest classifier. Then, to apply the transformation, we swap the top-K most relevant latent dimensions of the image being edited with an image exhibiting the desired attribute. Despite its simplicity, LatentSwap3D provides remarkable semantic edits in a disentangled manner and outperforms alternative approaches both qualitatively and quantitatively. We demonstrate our semantic edit approach on various 3D-aware generative models such as pi-GAN, GIRAFFE, StyleSDF, MVCGAN, EG3D and VolumeGAN, and on diverse datasets, such as FFHQ, AFHQ, Cats, MetFaces, and CompCars. The project page can be found: \url{https://enisimsar.github.io/latentswap3d/}.

使用单视图2D照片仅集合，无监督的高质量多视图 - 一致的图像和3D形状一直是一个长期存在的挑战。现有的3D GAN是计算密集型的，也是没有3D-一致的近似;前者限制了所生成的图像的质量和分辨率，并且后者对多视图一致性和形状质量产生不利影响。在这项工作中，我们提高了3D GAN的计算效率和图像质量，而无需依赖这些近似。为此目的，我们介绍了一种表现力的混合明确隐式网络架构，与其他设计选择一起，不仅可以实时合成高分辨率多视图一致图像，而且还产生高质量的3D几何形状。通过解耦特征生成和神经渲染，我们的框架能够利用最先进的2D CNN生成器，例如Stylega2，并继承它们的效率和表现力。在其他实验中，我们展示了与FFHQ和AFHQ猫的最先进的3D感知合成。

最近的研究表明，风格老年提供了对图像合成和编辑的下游任务的有希望的现有模型。然而，由于样式盖的潜在代码被设计为控制全球样式，因此很难实现对合成图像的细粒度控制。我们提出了SemanticStylegan，其中发电机训练以分别培训局部语义部件，并以组成方式合成图像。不同局部部件的结构和纹理由相应的潜在码控制。实验结果表明，我们的模型在不同空间区域之间提供了强烈的解剖。当与为样式器设计的编辑方法结合使用时，它可以实现更细粒度的控制，以编辑合成或真实图像。该模型也可以通过传输学习扩展到其他域。因此，作为具有内置解剖学的通用先前模型，它可以促进基于GaN的应用的发展并实现更多潜在的下游任务。

最近的图像匿名工作表明，生成的对抗性网络（GANS）可以生成近乎光容化的面对匿名的个人。但是，将这些网络扩展到整个人体仍然是一个具有挑战性和未解决的任务。我们提出了一种新的匿名化方法，用于为野外图像产生近乎光容化的人类。我们的设计的关键部分是通过在图像和规范3D表面之间的密集像素对应关系来引导对抗性网。我们介绍了各种表面自适应调制（V-SAM），该调制（V-SAM）在整个发电机中嵌入表面信息。通过我们的新型鉴别器表面监控损失，发电机可以合成高质量的人类，在复杂和不同的场景中具有多样化的外观。我们展示了这种表面指导显着提高了样本的图像质量和多样性，产生了高度实用的发电机。最后，我们证明了表面引导的匿名化保留了未来计算机视觉开发数据的可用性

The primary aim of single-image super-resolution is to construct a high-resolution (HR) image from a corresponding low-resolution (LR) input. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR images. Optimizing such metrics often leads to blurring, especially in high variance (detailed) regions. We propose an alternative formulation of the super-resolution problem based on creating realistic SR images that downscale correctly. We present a novel super-resolution algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature. It accomplishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require training on databases of LR-HR image pairs for supervised learning). Instead of starting with the LR image and slowly adding detail, PULSE traverses the high-resolution natural image manifold, searching for images that downscale to the original LR image. This is formalized through the "downscaling loss," which guides exploration through the latent space of a generative model. By leveraging properties of high-dimensional Gaussians, we restrict the search space to guarantee that our outputs are realistic. PULSE thereby generates super-resolved images that both are realistic and downscale correctly. We show extensive experimental results demonstrating the efficacy of our approach in the domain of face super-resolution (also known as face hallucination). We also present a discussion of the limitations and biases of the method as currently implemented with an accompanying model card with relevant metrics. Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously pos-sible.

2 Lambda Labs 3 Twitter Figure 1. HoloGAN learns to separate pose from identity (shape and appearance) only from unlabelled 2D images without sacrificing the visual fidelity of the generated images. All results shown here are sampled from HoloGAN for the same identities in each row but in different poses.

We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

由于简单但有效的训练机制和出色的图像产生质量，生成的对抗网络（GAN）引起了极大的关注。具有生成照片现实的高分辨率（例如$ 1024 \ times1024 $）的能力，最近的GAN模型已大大缩小了生成的图像与真实图像之间的差距。因此，许多最近的作品表明，通过利用良好的潜在空间和博学的gan先验来利用预先训练的GAN模型的新兴兴趣。在本文中，我们简要回顾了从三个方面利用预先培训的大规模GAN模型的最新进展，即1）大规模生成对抗网络的培训，2）探索和理解预训练的GAN模型，以及预先培训的GAN模型，以及3）利用这些模型进行后续任务，例如图像恢复和编辑。有关相关方法和存储库的更多信息，请访问https://github.com/csmliu/pretretaining-gans。