Neural Design Network: Graphic Layout Generation with Constraints
Abstract
Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation that can satisfy user-specified constraints. The proposed neural design network (NDN) consists of three modules. The first module predicts a graph with complete relations from a graph with user-specified relations. The second module generates a layout from the predicted graph. Finally, the third module fine-tunes the predicted layout. Quantitative and qualitative experiments demonstrate that the generated layouts are visually similar to real design layouts. We also construct real designs based on predicted layouts for a better understanding of the visual quality. Finally, we demonstrate a practical application on layout recommendation.
Cite
Text
Lee et al. "Neural Design Network: Graphic Layout Generation with Constraints." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58580-8_29Markdown
[Lee et al. "Neural Design Network: Graphic Layout Generation with Constraints." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/lee2020eccv-neural/) doi:10.1007/978-3-030-58580-8_29BibTeX
@inproceedings{lee2020eccv-neural,
title = {{Neural Design Network: Graphic Layout Generation with Constraints}},
author = {Lee, Hsin-Ying and Jiang, Lu and Essa, Irfan and Le, Phuong B and Gong, Haifeng and Yang, Ming-Hsuan and Yang, Weilong},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58580-8_29},
url = {https://mlanthology.org/eccv/2020/lee2020eccv-neural/}
}