Toward Realistic Image Compositing with Adversarial Learning

January 1, 2019

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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Publication Type

Paper

Conference / Journal Name

CVPR

Authors

Bor-Chun Chen

Andrew Kae

BibTeX


@inproceedings{
    author = {},
    title = {‌Toward Realistic Image Compositing with Adversarial Learning‌},
    booktitle = {Proceedings of CVPR‌},
    year = {‌2019‌}
}