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

Adversarial Scene Editing: Automatic Object Removal from Weak Supervision