Poster
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
Rakshith R Shetty · Mario Fritz · Bernt Schiele

Tue Dec 4th 10:45 AM -- 12:45 PM @ Room 210 #16
in Tue Poster Session A »

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.

Keywords: Semi-Supervised Learning · Unsupervised Learning · Computer Vision · Adversarial Networks · Generative Models

Author Information

Rakshith R Shetty (Max Planck Institute for Informatics)
Mario Fritz (CISPA Helmholtz Center i.G.)
Bernt Schiele (Max Planck Institute for Informatics)

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