We present a novel approach for synthesizing photo-realistic images of people
in arbitrary poses using generative adversarial learning. Given an input image
of a person and a desired pose represented by a 2D skeleton, our model renders
the image of the same person under the new pose, synthesizing novel views of
the parts visible in the input image and hallucinating those that are not seen.
This problem has recently been addressed in a supervised manner, i.e., during
training the ground truth images under the new poses are given to the network.
We go beyond these approaches by proposing a fully unsupervised strategy. We
tackle this challenging scenario by splitting the problem into two principal
subtasks. First, we consider a pose conditioned bidirectional generator that
maps back the initially rendered image to the original pose, hence being
directly comparable to the input image without the need to resort to any
training image. Second, we devise a novel loss function that incorporates
content and style terms, and aims at producing images of high perceptual
quality. Extensive experiments conducted on the DeepFashion dataset demonstrate
that the images rendered by our model are very close in appearance to those
obtained by fully supervised approaches.

本文提出了一种使用生成式对抗学习的新方法，用于综合产生任意姿势的具有照片级真实感的人物图像。研究通过无监督的方式来处理此问题，并且将其分解成两个主要子任务，一是姿势条件的双向生成器，二是定义一个新型的损失函数，包含内容和风格项，旨在生成高感知质量的图像，可以在 DeepFashion 数据集上进行广泛的实验验证。