Multi-domain image-to-image translation has gained increasing attention
recently. Previous methods take an image and some target attributes as inputs
and generate an output image with the desired attributes. However, such methods
have two limitations. First, these methods assume binary-valued attributes and
thus cannot yield satisfactory results for fine-grained control. Second, these
methods require specifying the entire set of target attributes, even if most of
the attributes would not be changed. To address these limitations, we propose
RelGAN, a new method for multi-domain image-to-image translation. The key idea
is to use relative attributes, which describes the desired change on selected
attributes. Our method is capable of modifying images by changing particular
attributes of interest in a continuous manner while preserving the other
attributes. Experimental results demonstrate both the quantitative and
qualitative effectiveness of our method on the tasks of facial attribute
transfer and interpolation.

论文提出了一种名为 RelGAN 的新的多领域图像到图像翻译方法，该方法使用相对属性来在连续的方式下修改图像，以达到更好的细粒度控制和有效性。在面部属性转移和插值任务中，实验结果表明了我们方法的量化和定性有效性。

RelGAN: 基于相对属性的多领域图像到图像翻译

RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes

We address the problem of instance-level facial attribute transfer without
paired training data, e.g. faithfully transferring the exact mustache from a
source face to a target face. This is a more challenging task than the
conventional semantic-level attribute transfer, which only preserves the
generic attribute style instead of instance-level traits. We propose the use of
geometry-aware flow, which serves as a well-suited representation for modeling
the transformation between instance-level facial attributes. Specifically, we
leverage the facial landmarks as the geometric guidance to learn the
differentiable flows automatically, despite of the large pose gap existed.
Geometry-aware flow is able to warp the source face attribute into the target
face context and generate a warp-and-blend result. To compensate for the
potential appearance gap between source and target faces, we propose a
hallucination sub-network that produces an appearance residual to further
refine the warp-and-blend result. Finally, a cycle-consistency framework
consisting of both attribute transfer module and attribute removal module is
designed, so that abundant unpaired face images can be used as training data.
Extensive evaluations validate the capability of our approach in transferring
instance-level facial attributes faithfully across large pose and appearance
gaps. Thanks to the flow representation, our approach can readily be applied to
generate realistic details on high-resolution images.

本篇论文提出了一种基于几何感知流表征的面部特征无对应数据实例级转移方法，配合外推网络和循环一致性框架可以实现跨大姿态和外貌差异的高保真面部特征转移。

具有几何感知流的实例级面部特征转移

Instance-level Facial Attributes Transfer with Geometry-Aware Flow

Existing methods for multi-domain image-to-image translation (or generation)
attempt to directly map an input image (or a random vector) to an image in one
of the output domains. However, most existing methods have limited scalability
and robustness, since they require building independent models for each pair of
domains in question. This leads to two significant shortcomings: (1) the need
to train exponential number of pairwise models, and (2) the inability to
leverage data from other domains when training a particular pairwise mapping.
Inspired by recent work on module networks, this paper proposes ModularGAN for
multi-domain image generation and image-to-image translation. ModularGAN
consists of several reusable and composable modules that carry on different
functions (e.g., encoding, decoding, transformations). These modules can be
trained simultaneously, leveraging data from all domains, and then combined to
construct specific GAN networks at test time, according to the specific image
translation task. This leads to ModularGAN's superior flexibility of generating
(or translating to) an image in any desired domain. Experimental results
demonstrate that our model not only presents compelling perceptual results but
also outperforms state-of-the-art methods on multi-domain facial attribute
transfer.

本文介绍了一种名为 ModularGAN 的多领域图像生成和图像到图像转换的方法，利用可复用和可组合的不同功能模块（例如编码、解码、变换）同时训练，并在测试时根据具体的图像转换任务组合这些模块，以显著提高生成（或转换为）任何所需领域图像的灵活性，并在多领域面部属性转移方面胜过现有方法。