Generative Adversarial Networks (GANs) have recently demonstrated the
capability to synthesize compelling real-world images, such as room interiors,
album covers, manga, faces, birds, and flowers. While existing models can
synthesize images based on global constraints such as a class label or caption,
they do not provide control over pose or object location. We propose a new
model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes
images given instructions describing what content to draw in which location. We
show high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset,
conditioned on both informal text descriptions and also object location. Our
system exposes control over both the bounding box around the bird and its
constituent parts. By modeling the conditional distributions over part
locations, our system also enables conditioning on arbitrary subsets of parts
(e.g. only the beak and tail), yielding an efficient interface for picking part
locations. We also show preliminary results on the more challenging domain of
text- and location-controllable synthesis of images of human actions on the
MPII Human Pose dataset.

Generative Adversarial Networks 已经在绘制逼真的世界图片方面展现了相当可观的能力，现有的模型可以根据类别标签或标题等全局限制生成图像，但不能控制姿势或对象位置，本文提出了一种新的模型 ——Generative Adversarial What-Where Network，它能根据所描述的内容和位置进行图像综合，并在 Caltech-UCSD Birds 数据集上展示了高质量的 128 x 128 图片综合，还可以控制鸟周围的边界框及其组成部分，通过模拟条件分布，我们的系统还能够使各个部分进行控制，同时展示了在 MPII Human Pose 数据集的关于人体动作图像的文本与位置可控合成的初步结果。