Generative Adversarial Networks (GANs) have shown success in approximating
complex distributions for synthetic image generation and for editing specific
portions of an input image, particularly in faces. However, current GAN-based
methods for generating biometric images, such as iris, have limitations in
controlling the identity of the generated images, i.e., the synthetically
generated images often closely resemble images in the training dataset.
Further, the generated images often lack diversity in terms of the number of
unique identities represented in them. To overcome these issues, we propose
iWarpGAN that disentangles identity and style in the context of the iris
modality by using two transformation pathways: Identity Transformation Pathway
to generate unique identities from the training set, and Style Transformation
Pathway to extract the style code from a reference image and output an iris
image using this style. By concatenating the transformed identity code and
reference style code, iWarpGAN generates iris images with both inter and
intra-class variations. The efficacy of the proposed method in generating Iris
DeepFakes is evaluated both qualitatively and quantitatively using ISO/IEC
29794-6 Standard Quality Metrics and the VeriEye iris matcher. Finally, the
utility of the synthetically generated images is demonstrated by improving the
performance of multiple deep learning based iris matchers that augment
synthetic data with real data during the training process.

该论文提出了 iWarpGAN 方法，利用两种转换通路（身份转换路径和风格转换路径）解耦虹膜图像中的身份和风格，生成具有身份和风格变异的合成虹膜图像，并使用合成数据增强深度学习算法的性能。

iWarpGAN：解耦身份和风格以生成合成虹膜图像

iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris  Images

This paper is on face/head reenactment where the goal is to transfer the
facial pose (3D head orientation and expression) of a target face to a source
face. Previous methods focus on learning embedding networks for identity and
pose disentanglement which proves to be a rather hard task, degrading the
quality of the generated images. We take a different approach, bypassing the
training of such networks, by using (fine-tuned) pre-trained GANs which have
been shown capable of producing high-quality facial images. Because GANs are
characterized by weak controllability, the core of our approach is a method to
discover which directions in latent GAN space are responsible for controlling
facial pose and expression variations. We present a simple pipeline to learn
such directions with the aid of a 3D shape model which, by construction,
already captures disentangled directions for facial pose, identity and
expression. Moreover, we show that by embedding real images in the GAN latent
space, our method can be successfully used for the reenactment of real-world
faces. Our method features several favorable properties including using a
single source image (one-shot) and enabling cross-person reenactment. Our
qualitative and quantitative results show that our approach often produces
reenacted faces of significantly higher quality than those produced by
state-of-the-art methods for the standard benchmarks of VoxCeleb1 & 2. Source
code is available at:
this https URL

该研究探讨了如何使用预训练 GAN 模型实现头部和面部重新实现，并使用 3D 形状模型帮助发现潜在空间中的方向，以达到身份解缠和跨人重现的目的。研究结果表明，与现有的最先进方法相比，该方法是一种高质量的方法。

GAN 隐空间中的方向搜索用于神经网络人脸再现

Finding Directions in GAN's Latent Space for Neural Face Reenactment

Learning disentangled representations of data is a fundamental problem in
artificial intelligence. Specifically, disentangled latent representations
allow generative models to control and compose the disentangled factors in the
synthesis process. Current methods, however, require extensive supervision and
training, or instead, noticeably compromise quality. In this paper, we present
a method that learns how to represent data in a disentangled way, with minimal
supervision, manifested solely using available pre-trained networks. Our key
insight is to decouple the processes of disentanglement and synthesis, by
employing a leading pre-trained unconditional image generator, such as
StyleGAN. By learning to map into its latent space, we leverage both its
state-of-the-art quality, and its rich and expressive latent space, without the
burden of training it. We demonstrate our approach on the complex and high
dimensional domain of human heads. We evaluate our method qualitatively and
quantitatively, and exhibit its success with de-identification operations and
with temporal identity coherency in image sequences. Through extensive
experimentation, we show that our method successfully disentangles identity
from other facial attributes, surpassing existing methods, even though they
require more training and supervision.

本文提供了一种使用预训练网络来学习数据的解缠表示的方法，以实现最小的监督，同时展示了该方法在头部图像领域上成功将身份从其他面部属性中解缠并显示出较好的评估结果。