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We address the problem of finding realistic geometric corrections to aforeground object such that it appears natural when composited into abackground image. To achieve this, we propose a novel Generative AdversarialNetwork (GAN) architecture that utilizes Spatial Transformer Networks (STNs) asthe generator, which we call Spatial Transformer GANs (ST-GANs). ST-GANs seekimage realism by operating in the geometric warp parameter space. Inparticular, we exploit an iterative STN warping scheme and propose a sequentialtraining strategy that achieves better results compared to naive training of asingle generator. One of the key advantages of ST-GAN is its applicability tohigh-resolution images indirectly since the predicted warp parameters aretransferable between reference frames. We demonstrate our approach in twoapplications: (1) visualizing how indoor furniture (e.g. from product images)might be perceived in a room, (2) hallucinating how accessories like glasseswould look when matched with real portraits.

ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing