Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.

我们提出了一种将任意样式图像的艺术特征转移到3D场景的方法。在点云或网格上执行3D风格的先前方法对复杂的现实世界场景的几何重建错误敏感。取而代之的是，我们建议对更健壮的辐射场字段表示。我们发现，常用的基于克矩阵的损失倾向于在没有忠实笔触的情况下产生模糊的结果，并引入了最近的基于邻居的损失，该损失非常有效地捕获样式的细节，同时保持多视图一致性。我们还提出了一种新颖的递延后传播方法，以使用在全分辨率渲染图像上定义的样式损失来优化记忆密集型辐射场。我们广泛的评估表明，我们的方法通过产生与样式图像更相似的艺术外观来优于基线。请检查我们的项目页面以获取视频结果和开源实现：https：//www.cs.cornell.edu/projects/arf/。

任意样式转移生成了艺术图像，该图像仅使用一个训练有素的网络结合了内容图像的结构和艺术风格的结合。此方法中使用的图像表示包含内容结构表示和样式模式表示形式，这通常是预训练的分类网络中高级表示的特征表示。但是，传统的分类网络是为分类而设计的，该分类通常集中在高级功能上并忽略其他功能。结果，风格化的图像在整个图像中均匀地分布了样式元素，并使整体图像结构无法识别。为了解决这个问题，我们通过结合全球和局部损失，引入了一种新型的任意风格转移方法，并通过结构增强。局部结构细节由LapStyle表示，全局结构由图像深度控制。实验结果表明，与其他最新方法相比，我们的方法可以在几个常见数据集中生成具有令人印象深刻的视觉效果的更高质量图像。

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

作为非遗迹渲染（NPR）的主要分支，图像样式主要使用计算机算法将照片渲染为艺术绘画。最近的工作表明，样式信息的提取，例如笔触纹理和目标样式图像的颜色是图像风格的关键。鉴于其中风质地和颜色特征，提出了一种新的中风渲染方法，该方法完全考虑了音调特征和原始油画的代表性，以便将原始油画图像的音调适应风格化的图像并制作它接近艺术家的创造性效果。实验验证了所提出模型的功效。这种方法更适合具有相对均匀的方向意识的点尔主义画家的作品，尤其是对于自然场景。当原始绘画笔触具有更清晰的方向感时，使用此方法模拟刷子纹理特征可能会不那么令人满意。

神经风格转移（NST）与视觉媒体的艺术风格有关。它可以描述为将艺术图像风格转移到普通照片上的过程。最近，许多研究考虑了NST算法的深度保护功能的增强，以解决当输入内容图像包含许多深度的众多对象时发生的不希望的效果。我们的方法使用了一个深层残留卷积网络，并使用实例归一化层，该层利用高级深度预测网络将深度保存作为内容和样式的附加损失函数集成。我们展示了有效保留内容图像的深度和全局结构的结果。三个不同的评估过程表明，我们的系统能够保留风格化结果的结构，同时表现出样式捕捉功能和美学质量，或与最先进的方法相当或优越。项目页面：https：//ioannoue.github.io/depth-aware-nst-using-in.html。

This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feedforward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic texture, or photos to artistic paintings. With adversarial training, we obtain quality comparable to recent neural texture synthesis methods. As no optimization is required any longer at generation time, our run-time performance (0.25M pixel images at 25Hz) surpasses previous neural texture synthesizers by a significant margin (at least 500 times faster). We apply this idea to texture synthesis, style transfer, and video stylization.

STYLE TRANSED引起了大量的关注，因为它可以在保留图像结构的同时将给定图像更改为一个壮观的艺术风格。然而，常规方法容易丢失图像细节，并且在风格转移期间倾向于产生令人不快的伪影。在本文中，为了解决这些问题，提出了一种具有目标特征调色板的新颖艺术程式化方法，可以准确地传递关键特征。具体而言，我们的方法包含两个模块，即特征调色板组成（FPC）和注意着色（AC）模块。 FPC模块基于K-means群集捕获代表特征，并生成特征目标调色板。以下AC模块计算内容和样式图像之间的注意力映射，并根据注意力映射和目标调色板传输颜色和模式。这些模块使提出的程式化能够专注于关键功能并生成合理的传输图像。因此，所提出的方法的贡献是提出一种新的深度学习的样式转移方法和当前目标特征调色板和注意着色模块，并通过详尽的消融研究提供对所提出的方法的深入分析和洞察。定性和定量结果表明，我们的程式化图像具有最先进的性能，具有保护核心结构和内容图像的细节。

Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the human perception of image quality. As a result, algorithms minimizing these metrics tend to produce over-smoothed images that lack highfrequency textures and do not look natural despite yielding high PSNR values.We propose a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixelaccurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios. Extensive experiments on a number of datasets show the effectiveness of our approach, yielding state-of-the-art results in both quantitative and qualitative benchmarks.

基于图像的艺术渲染可以使用算法图像过滤合成各种表达式。与基于深度学习的方法相反，这些基于启发式的过滤技术可以在高分辨率图像上运行，可以解释，并且可以根据各个设计方面进行参数化。但是，适应或扩展这些技术生产新样式通常是一项乏味且容易出错的任务，需要专家知识。我们提出了一个新的范式来减轻此问题：实现算法图像过滤技术作为可区分的操作，可以学习与某些参考样式一致的参数化。为此，我们提出了明智的，这是一个基于示例的图像处理系统，可以在公共框架内处理多种风格化技术，例如水彩，油或卡通风格。通过训练全局和本地滤波器参数化的参数预测网络，我们可以同时适应参考样式和图像内容，例如增强面部特征。我们的方法可以在样式转移框架中进行优化，也可以在用于图像到图像翻译的生成对流设置中学习。我们证明，共同训练XDOG滤波器和用于后处理的CNN可以与基于GAN的最新方法获得可比的结果。

This paper creates a novel method of deep neural style transfer by generating style images from freeform user text input. The language model and style transfer model form a seamless pipeline that can create output images with similar losses and improved quality when compared to baseline style transfer methods. The language model returns a closely matching image given a style text and description input, which is then passed to the style transfer model with an input content image to create a final output. A proof-of-concept tool is also developed to integrate the models and demonstrate the effectiveness of deep image style transfer from freeform text.

Neural style transfer is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image and is particularly impressive when it comes to transferring style from a painting to an image. It was originally achieved by solving an optimization problem to match the global style statistics of the style image while preserving the local geometric features of the content image. The two main drawbacks of this original approach is that it is computationally expensive and that the resolution of the output images is limited by high GPU memory requirements. Many solutions have been proposed to both accelerate neural style transfer and increase its resolution, but they all compromise the quality of the produced images. Indeed, transferring the style of a painting is a complex task involving features at different scales, from the color palette and compositional style to the fine brushstrokes and texture of the canvas. This paper provides a solution to solve the original global optimization for ultra-high resolution images, enabling multiscale style transfer at unprecedented image sizes. This is achieved by spatially localizing the computation of each forward and backward passes through the VGG network. Extensive qualitative and quantitative comparisons show that our method produces a style transfer of unmatched quality for such high resolution painting styles.

Arbitrary Style Transfer is a technique used to produce a new image from two images: a content image, and a style image. The newly produced image is unseen and is generated from the algorithm itself. Balancing the structure and style components has been the major challenge that other state-of-the-art algorithms have tried to solve. Despite all the efforts, it's still a major challenge to apply the artistic style that was originally created on top of the structure of the content image while maintaining consistency. In this work, we solved these problems by using a Deep Learning approach using Convolutional Neural Networks. Our implementation will first extract foreground from the background using the pre-trained Detectron 2 model from the content image, and then apply the Arbitrary Style Transfer technique that is used in SANet. Once we have the two styled images, we will stitch the two chunks of images after the process of style transfer for the complete end piece.

回想一下，大多数当前图像样式转移方法要求用户给出特定样式的图像，然后提取该样式功能和纹理以生成图像的样式，但仍然存在一些问题：用户可能没有一个参考样式图像，或者很难用一个图像总结所需的样式。最近提议的夹板解决了此问题，该问题仅根据提供的样式图像的描述来执行样式转移。尽管当景观或肖像单独出现时，ClipStyler可以取得良好的性能，但它可能会模糊人民并在人和风景共存时失去原始语义。基于这些问题，我们演示了一个新颖的框架，该框架使用了预训练的剪辑文本图像嵌入模型，并通过FCN语义分割网络指导图像样式传输。具体而言，我们解决了与人类主题相机的自拍照和现实世界的肖像过度风格的问题，增强了肖像和景观风格转移效果之间的对比，并使不同语义部分的图像风格转移程度完全可控。我们的生成工匠解决了夹具的失败案例，并产生定性和定量方法，以证明我们在自拍照和人类受试者照片中的自拍照和现实世界景观中的剪贴画的结果要好得多。这种改进使我们可以将我们的业务场景框架（例如修饰图形软件）进行商业化。

图像或视频外观特征（例如颜色，纹理，音调，照明等）反映了一个人的视觉感知和对图像或视频的直接印象。给定的源图像（视频）和目标图像（视频），图像（视频）颜色传输技术旨在处理源图像或视频的颜色（请注意，源图像或视频也引用了参考图像或一些文献中的视频）使它看起来像目标图像或视频的视频，即将目标图像或视频的外观传输到源图像或视频的外观，从而可以改变对源图像或视频的感知。作为色彩传输的扩展，样式转移是指以风格样本或通过样式传输模型的样式样本或一组图像的艺术家的样式呈现目标图像或视频的内容。作为一个新兴领域，对风格转移的研究吸引了许多研究人员的注意。经过数十年的发展，它已成为一项高度的跨学科研究，并可以实现各种艺术表达方式。本文概述了过去几年的色彩传输和样式转移方法。

现有的神经样式传输方法需要参考样式图像来将样式图像的纹理信息传输到内容图像。然而，在许多实际情况中，用户可能没有参考样式图像，但仍然有兴趣通过想象它们来传输样式。为了处理此类应用程序，我们提出了一个新的框架，它可以实现样式转移`没有'风格图像，但仅使用所需风格的文本描述。使用预先训练的文本图像嵌入模型的剪辑，我们仅通过单个文本条件展示了内容图像样式的调制。具体而言，我们提出了一种针对现实纹理传输的多视图增强的修补程序文本图像匹配丢失。广泛的实验结果证实了具有反映语义查询文本的现实纹理的成功图像风格转移。

图像转换是一类视觉和图形问题，其目标是学习输入图像和输出图像之间的映射，在深神网络的背景下迅速发展。在计算机视觉（CV）中，许多问题可以被视为图像转换任务，例如语义分割和样式转移。这些作品具有不同的主题和动机，使图像转换任务蓬勃发展。一些调查仅回顾有关样式转移或图像到图像翻译的研究，所有这些都只是图像转换的一个分支。但是，没有一项调查总结这些调查在我们最佳知识的统一框架中共同起作用。本文提出了一个新颖的学习框架，包括独立学习，指导学习和合作学习，称为IGC学习框架。我们讨论的图像转换主要涉及有关深神经网络的一般图像到图像翻译和样式转移。从这个框架的角度来看，我们回顾了这些子任务，并对各种情况进行统一的解释。我们根据相似的开发趋势对图像转换的相关子任务进行分类。此外，已经进行了实验以验证IGC学习的有效性。最后，讨论了新的研究方向和开放问题，以供将来的研究。

Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited. In this paper we conduct a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself? To answer this question we contribute a general framework to invert representations. We show that this method can invert representations such as HOG and SIFT more accurately than recent alternatives while being applicable to CNNs too. We then use this technique to study the inverse of recent state-of-the-art CNN image representations for the first time. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image superresolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4× upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.