Toward Realistic Image Compositing With Adversarial Learning

Bor-Chun Chen, Andrew Kae; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 8415-8424

Abstract


Compositing a realistic image is a challenging task and usually requires considerable human supervision using professional image editing software. In this work we propose a generative adversarial network (GAN) architecture for automatic image compositing. The proposed model consists of four sub-networks: a transformation network that improves the geometric and color consistency of the composite image, a refinement network that polishes the boundary of the composite image, and a pair of discriminator network and a segmentation network for adversarial learning. Experimental results on both synthesized images and real images show that our model, Geometrically and Color Consistent GANs (GCC-GANs), can automatically generate realistic composite images compared to several state-of-the-art methods, and does not require any manual effort.

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[bibtex]
@InProceedings{Chen_2019_CVPR,
author = {Chen, Bor-Chun and Kae, Andrew},
title = {Toward Realistic Image Compositing With Adversarial Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}