In this paper, we propose a novel framework to translate a portrait
photo-face into an anime appearance. Our aim is to synthesize anime-faces which
are style-consistent with a given reference anime-face. However, unlike typical
translation tasks, such anime-face translation is challenging due to complex
variations of appearances among anime-faces. Existing methods often fail to
transfer the styles of reference anime-faces, or introduce noticeable
artifacts/distortions in the local shapes of their generated faces. We propose
AniGAN, a novel GAN-based translator that synthesizes high-quality anime-faces.
Specifically, a new generator architecture is proposed to simultaneously
transfer color/texture styles and transform local facial shapes into anime-like
counterparts based on the style of a reference anime-face, while preserving the
global structure of the source photo-face. We propose a double-branch
discriminator to learn both domain-specific distributions and domain-shared
distributions, helping generate visually pleasing anime-faces and effectively
mitigate artifacts. Extensive experiments on selfie2anime and a new face2anime
dataset qualitatively and quantitatively demonstrate the superiority of our
method over state-of-the-art methods. The new dataset is available at
this https URL .

该研究提出了一个基于 GAN 的新框架，可以将肖像照片转换为动漫外观的高质量图像，同时保留源图像的整体结构，该框架采用了双分支鉴别器来学习具体领域分布和领域共享分布，有助于生成具有视觉吸引力的动漫图像。

AniGAN: 面向风格引导的生成对抗网络 (Unsupervised) 在动漫头像生成中的应用

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised  Anime Face Generation

Deepfakes are a recent off-the-shelf manipulation technique that allows
anyone to swap two identities in a single video. In addition to Deepfakes, a
variety of GAN-based face swapping methods have also been published with
accompanying code. To counter this emerging threat, we have constructed an
extremely large face swap video dataset to enable the training of detection
models, and organized the accompanying DeepFake Detection Challenge (DFDC)
Kaggle competition. Importantly, all recorded subjects agreed to participate in
and have their likenesses modified during the construction of the face-swapped
dataset. The DFDC dataset is by far the largest currently and publicly
available face swap video dataset, with over 100,000 total clips sourced from
3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned
methods. In addition to describing the methods used to construct the dataset,
we provide a detailed analysis of the top submissions from the Kaggle contest.
We show although Deepfake detection is extremely difficult and still an
unsolved problem, a Deepfake detection model trained only on the DFDC can
generalize to real "in-the-wild" Deepfake videos, and such a model can be a
valuable analysis tool when analyzing potentially Deepfaked videos. Training,
validation and testing corpuses can be downloaded from
this https URL

本文介绍了构建深度伪造检测模型的关键 - 构建大规模的人脸置换视频数据集和 Deepfakes 检测挑战。

深度假象检测挑战赛数据集 (DFDC)

The DeepFake Detection Challenge (DFDC) Dataset

We propose an interactive GAN-based sketch-to-image translation method that
helps novice users create images of simple objects. As the user starts to draw
a sketch of a desired object type, the network interactively recommends
plausible completions, and shows a corresponding synthesized image to the user.
This enables a feedback loop, where the user can edit their sketch based on the
network's recommendations, visualizing both the completed shape and final
rendered image while they draw. In order to use a single trained model across a
wide array of object classes, we introduce a gating-based approach for class
conditioning, which allows us to generate distinct classes without feature
mixing, from a single generator network. Video available at our website:
this https URL

本文提出了一种交互式的基于 GAN 的草图到图像的翻译方法，能够帮助新手用户创建简单物体的图像，通过引入基于门控机制的分类条件方法，可以在不混合特征的情况下从单个生成器网络中生成不同的类别。

交互式素描填充：多类素描到图像翻译

Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation

Although image inpainting, or the art of repairing the old and deteriorated
images, has been around for many years, it has gained even more popularity
because of the recent development in image processing techniques. With the
improvement of image processing tools and the flexibility of digital image
editing, automatic image inpainting has found important applications in
computer vision and has also become an important and challenging topic of
research in image processing. This paper is a brief review of the existing
image inpainting approaches we first present a global vision on the existing
methods for image inpainting. We attempt to collect most of the existing
approaches and classify them into three categories, namely, sequential-based,
CNN-based and GAN-based methods. In addition, for each category, a list of
methods for the different types of distortion on the images is presented.
Furthermore, collect a list of the available datasets and discuss these in our
paper. This is a contribution for digital image inpainting researchers trying
to look for the available datasets because there is a lack of datasets
available for image inpainting. As the final step in this overview, we present
the results of real evaluations of the three categories of image inpainting
methods performed on the datasets used, for the different types of image
distortion. In the end, we also present the evaluations metrics and discuss the
performance of these methods in terms of these metrics. This overview can be
used as a reference for image inpainting researchers, and it can also
facilitate the comparison of the methods as well as the datasets used. The main
contribution of this paper is the presentation of the three categories of image
inpainting methods along with a list of available datasets that the researchers
can use to evaluate their proposed methodology against.

本文是对已有图像修复方法的分析以及分类，分为基于顺序、基于 CNN 和基于 GAN 的方法，并提供不同类型图像失真的方法列表和可用数据集，为数字图像修复研究者提供了参考和方便了方法的比较和数据集的使用。