Advertisement Logo Compositing via Adversarial Geometric Consistency Pursuit Advertisement logo compositing is aiming to embed some specified logos in a suitable position of target images with alike geometric distortion and same appearance characteristic. Unsupervised learning has gained considerable attention recently. To synthetically address the problems about geometric distortion and appearance realism, we propose a novel learnable module, the adversarial geometric consistency pursuit model (AGCP), which explicitly allows seamless image compositing. On one hand, we design an adversarial structure that generates composite images taking geometric correction and appearance harmonization into account. Our proposed adversarial learning approach is able to obtain better harmonization in the region of interest. On the other hand, a novel geometric consistency pursuit loss is designed which encourages the network to learn the warp parameters of target images while preserving the feature of the source object. Our comparative evaluation demonstrates the effectiveness of the proposed method.

GANILLA: Generative adversarial networks for image to illustration translation Abstract   In this paper, we explore illustrations in childrenu0027s books as a new domain in unpaired imagetoimage translation. We show that although the current stateoftheart imagetoimage translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content.  There are no welldefined or agreedupon evaluation metrics for unpaired imagetoimage translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of imagetoillustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current stateoftheart models on the illustrations dataset. Our code and pretrained models can be found at  https:github.comgiddyyuppganilla .