While large-scale pre-trained text-to-image models can synthesize diverse and
high-quality human-centric images, an intractable problem is how to preserve
the face identity for conditioned face images. Existing methods either require
time-consuming optimization for each face-identity or learning an efficient
encoder at the cost of harming the editability of models. In this work, we
present an optimization-free method for each face identity, meanwhile keeping
the editability for text-to-image models. Specifically, we propose a novel
face-identity encoder to learn an accurate representation of human faces, which
applies multi-scale face features followed by a multi-embedding projector to
directly generate the pseudo words in the text embedding space. Besides, we
propose self-augmented editability learning to enhance the editability of
models, which is achieved by constructing paired generated face and edited face
images using celebrity names, aiming at transferring mature ability of
off-the-shelf text-to-image models in celebrity faces to unseen faces.
Extensive experiments show that our methods can generate identity-preserved
images under different scenes at a much faster speed.

该论文提出了一种优化自由的面部识别编码器，以在条件面部图像中保留面部标识符，并提出了自我增强的可编辑性学习方法，从而在更快的速度下生成具有不同场景下的保留标识符的图像。

DreamIdentity：提高可编辑性，实现高效面部身份保留图像生成

DreamIdentity: Improved Editability for Efficient Face-identity  Preserved Image Generation

This work addresses the problem of anonymizing the identity of faces in a
dataset of images, such that the privacy of those depicted is not violated,
while at the same time the dataset is useful for downstream task such as for
training machine learning models. To the best of our knowledge, we are the
first to explicitly address this issue and deal with two major drawbacks of the
existing state-of-the-art approaches, namely that they (i) require the costly
training of additional, purpose-trained neural networks, and/or (ii) fail to
retain the facial attributes of the original images in the anonymized
counterparts, the preservation of which is of paramount importance for their
use in downstream tasks. We accordingly present a task-agnostic anonymization
procedure that directly optimizes the images' latent representation in the
latent space of a pre-trained GAN. By optimizing the latent codes directly, we
ensure both that the identity is of a desired distance away from the original
(with an identity obfuscation loss), whilst preserving the facial attributes
(using a novel feature-matching loss in FaRL's deep feature space). We
demonstrate through a series of both qualitative and quantitative experiments
that our method is capable of anonymizing the identity of the images whilst --
crucially -- better-preserving the facial attributes. We make the code and the
pre-trained models publicly available at: this https URL

本文提出了一种针对人脸图像数据集的匿名处理方案，采用生成对抗网络的潜在空间进行优化，解决现有方案中需要额外训练神经网络、没有保存面部特征等问题，实现了既保护隐私，又适合于训练机器学习模型的效果。