Neural Image Recolorization for Creative Domains
Abstract
We present a self-supervised approach to recolorization of images from design-oriented domains. Our approach can recolor images based on image exemplars or target color palettes provided by a user. In contrast with previous approaches, our method can reproduce color palettes with luminance distributions that differ significantly from input, and our method is the first palette-based approach to distinguish between recolorings that match reflectance and those that match illumination, making it particularly well-suited to visualizing different aesthetic decisions in design applications. The key to our approach is first to learn latent representations for texture and color in a setting where self-supervision is especially straightforward, and then to learn a mapping to our color representation from input color palettes and scene illumination, which offers a more intuitive space for controlling and exploring recolorization.
Cite
Text
Li et al. "Neural Image Recolorization for Creative Domains." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00242Markdown
[Li et al. "Neural Image Recolorization for Creative Domains." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/li2022cvprw-neural/) doi:10.1109/CVPRW56347.2022.00242BibTeX
@inproceedings{li2022cvprw-neural,
title = {{Neural Image Recolorization for Creative Domains}},
author = {Li, Boyi and Belongie, Serge J. and Lim, Ser-Nam and Davis, Abe},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2022},
pages = {2225-2229},
doi = {10.1109/CVPRW56347.2022.00242},
url = {https://mlanthology.org/cvprw/2022/li2022cvprw-neural/}
}