This paper studies the task of full generative modelling of realistic images
of humans, guided only by coarse sketch of the pose, while providing control
over the specific instance or type of outfit worn by the user. This is a
difficult problem because input and output domain are very different and direct
image-to-image translation becomes infeasible. We propose an end-to-end
trainable network under the generative adversarial framework, that provides
detailed control over the final appearance while not requiring paired training
data and hence allows us to forgo the challenging problem of fitting 3D poses
to 2D images. The model allows to generate novel samples conditioned on either
an image taken from the target domain or a class label indicating the style of
clothing (e.g., t-shirt). We thoroughly evaluate the architecture and the
contributions of the individual components experimentally. Finally, we show in
a large scale perceptual study that our approach can generate realistic looking
images and that participants struggle in detecting fake images versus real
samples, especially if faces are blurred.

本研究旨在通过限定人体的粗略轮廓并控制特定服装类型实现真实人体图像的完整生成建模。我们提出了一种端到端可训练的生成式对抗网络架构，它提供了细节控制来生成具有高逼真度的图片，同时无需成对的训练数据，不必拟合 3D 姿势到 2D 图像的困难问题。该模型允许对基于目标领域的图像或标记特定的类别样式（例如 T 恤）进行条件生成。我们对该架构和每个独立要素的贡献进行了充分的实验评估，并通过大规模知觉研究表明，我们的方法可以生成逼真的图像，并且如果面部被模糊化，参与者很难识别真实的图片和虚假的图片之间的区别。

无配对姿势引导的人类图像生成

Unpaired Pose Guided Human Image Generation

When creating an outfit, style is a criterion in selecting each fashion item.
This means that style can be regarded as a feature of the overall outfit.
However, in various previous studies on outfit generation, there have been few
methods focusing on global information obtained from an outfit. To address this
deficiency, we have incorporated an unsupervised style extraction module into a
model to learn outfits. Using the style information of an outfit as a whole,
the proposed model succeeded in generating outfits more flexibly without
requiring additional information. Moreover, the style information extracted by
the proposed model is easy to interpret. The proposed model was evaluated on
two human-generated outfit datasets. In a fashion item prediction task (missing
prediction task), the proposed model outperformed a baseline method. In a style
extraction task, the proposed model extracted some easily distinguishable
styles. In an outfit generation task, the proposed model generated an outfit
while controlling its styles. This capability allows us to generate fashionable
outfits according to various preferences.

本研究提出了一种利用无监督风格提取模块来学习装扮的模型，可以更灵活地生成时尚的 outfit，同时成功地在时尚元素预测、风格提取和 outfit 生成任务中超越了基线方法。