We present a method for generating alpha mattes using a limited data source.
We pretrain a novel transformerbased model (StyleMatte) on portrait datasets.
We utilize this model to provide image-mask pairs for the StyleGAN3- based
network (StyleMatteGAN). This network is trained unsupervisedly and generates
previously unseen imagemask training pairs that are fed back to StyleMatte. We
demonstrate that the performance of the matte pulling network improves during
this cycle and obtains top results on the used datasets. Furthermore,
StyleMatteGAN provides high-resolution, privacy-preserving portraits with alpha
mattes, making it suitable for various image composition tasks. Our code is
available at this https URL

本研究利用预训练的转换器模型（StyleMatte）为基于 StyleGAN3 的网络（StyleMatteGAN）提供图像 - 掩码对，这个网络可用于无监督训练并生成高分辨率的带透明通道的肖像，适用于各种图像合成任务。

对抗引导下的人像抠图

Adversarially-Guided Portrait Matting

In the last few years, we have witnessed the rise of a series of deep
learning methods to generate synthetic images that look extremely realistic.
These techniques prove useful in the movie industry and for artistic purposes.
However, they also prove dangerous if used to spread fake news or to generate
fake online accounts. For this reason, detecting if an image is an actual
photograph or has been synthetically generated is becoming an urgent necessity.
This paper proposes a detector of synthetic images based on an ensemble of
Convolutional Neural Networks (CNNs). We consider the problem of detecting
images generated with techniques not available at training time. This is a
common scenario, given that new image generators are published more and more
frequently. To solve this issue, we leverage two main ideas: (i) CNNs should
provide orthogonal results to better contribute to the ensemble; (ii) original
images are better defined than synthetic ones, thus they should be better
trusted at testing time. Experiments show that pursuing these two ideas
improves the detector accuracy on NVIDIA's newly generated StyleGAN3 images,
never used in training.

本文提出了一种基于卷积神经网络集成的合成图像检测器，利用两个主要思想解决检测新的图像生成器的常见情况，结果表明这种方法提高了在 NVIDIA 的新生成 StyleGAN3 图像上的检测准确性。

通过多个 CNN 的正交训练检测 GAN 生成的图像

Detecting GAN-generated Images by Orthogonal Training of Multiple CNNs

StyleGAN is arguably one of the most intriguing and well-studied generative
models, demonstrating impressive performance in image generation, inversion,
and manipulation. In this work, we explore the recent StyleGAN3 architecture,
compare it to its predecessor, and investigate its unique advantages, as well
as drawbacks. In particular, we demonstrate that while StyleGAN3 can be trained
on unaligned data, one can still use aligned data for training, without
hindering the ability to generate unaligned imagery. Next, our analysis of the
disentanglement of the different latent spaces of StyleGAN3 indicates that the
commonly used W/W+ spaces are more entangled than their StyleGAN2 counterparts,
underscoring the benefits of using the StyleSpace for fine-grained editing.
Considering image inversion, we observe that existing encoder-based techniques
struggle when trained on unaligned data. We therefore propose an encoding
scheme trained solely on aligned data, yet can still invert unaligned images.
Finally, we introduce a novel video inversion and editing workflow that
leverages the capabilities of a fine-tuned StyleGAN3 generator to reduce
texture sticking and expand the field of view of the edited video.

本研究探讨了最新的 StyleGAN3 与其前任的区别、优缺点，研究了其潜在空间的分离性，并介绍了一种基于 StyleGAN3 的视频反演和编辑工作流。