While great progress has been made recently in automatic image manipulation,
it has been limited to object centric images like faces or structured scene
datasets. In this work, we take a step towards general scene-level image
editing by developing an automatic interaction-free object removal model. Our
model learns to find and remove objects from general scene images using
image-level labels and unpaired data in a generative adversarial network (GAN)
framework. We achieve this with two key contributions: a two-stage editor
architecture consisting of a mask generator and image in-painter that
co-operate to remove objects, and a novel GAN based prior for the mask
generator that allows us to flexibly incorporate knowledge about object shapes.
We experimentally show on two datasets that our method effectively removes a
wide variety of objects using weak supervision only

本文旨在研发一种自动化、不需要交互即可实现目标物体移除的模型，其基于生成对抗网络框架，利用图像级别标签和非配对数据进行训练。通过两个关键贡献：一个由掩膜生成器和图像修复器组成的两阶段编辑器体系结构，以及一种基于 GAN 的先验模型，该模型能灵活地整合物体形状知识。我们在两个数据集上进行实验表明，该方法能够有效地去除各种不同的物体，仅需要较弱的监督学习。