Image composition toolbox: everything you want to know about image composition or object insertion
We co-founded a startup company miguo.ai, dedicated to accelerating the production of comics and animations using AIGC technology. If you are seeking internship or full-time positions, please feel free to send your resume to [email protected].
libcom (the library of image composition) is an image composition toolbox. The goal of image composition (object insertion) is inserting one foreground into a background image to get a realistic composite image, by addressing the inconsistencies (appearance, geometry, and semantic inconsistency) between foreground and background. Generally speaking, image composition could be used to combine the visual elements from different images.
libcom covers a diversity of related tasks in the field of image composition, including image blending, standard/painterly image harmonization, shadow generation, object placement, generative composition, quality evaluation, etc. For each task, we integrate one or two selected methods considering both efficiency and effectiveness. The selected methods will be continuously updated upon the emergence of better methods.
The ultimate goal of this library is solving all the problems related to image composition with simple import libcom
.
For the detailed method descriptions, code examples, visualization results, and performance comments, please refer to our [documents].
The main branch is built on the Linux system with Python 3.8 and PyTorch>=1.10.1. For other dependencies, please refer to [conda_env] and [runtime_dependencies].
Please refer to [Installation] for installation instructions and [documents] for user guidance.
This project is released under the Apache 2.0 license.
If you use our toolbox, please cite our survey paper using the following BibTeX [arxiv]:
@article{niu2021making,
title={Making images real again: A comprehensive survey on deep image composition},
author={Niu, Li and Cong, Wenyan and Liu, Liu and Hong, Yan and Zhang, Bo and Liang, Jing and Zhang, Liqing},
journal={arXiv preprint arXiv:2106.14490},
year={2021}
}