Paper in ECCV 2020 on “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.

Citation

H. Lee, W. Yang, L. Jiang, M. Le, I. Essa, H. Gong, and M. Yang (2020), “Neural Design Network: Graphic Layout Generation with Constraints,” in Proceedings of European Conference on Computer Vision (ECCV), 2020. [PDF] [DOI] [arXiv] [BIBTEX]

@InProceedings{ 2020-Lee-NDNGLGWC,
archiveprefix  = {arXiv},
arxiv = {https://arxiv.org/abs/1912.09421},
author  = {Hsin-Ying Lee and Weilong Yang and Lu Jiang and
Madison Le and Irfan Essa and Haifeng Gong and
Ming-Hsuan Yang},
booktitle  = {{Proceedings of European Conference on Computer
Vision (ECCV)}},
doi = {10.1007/978-3-030-58580-8_29},
eprint  = {1912.09421},
month = {August},
pdf = {http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480494.pdf},
primaryclass  = {cs.CV},
title = {Neural Design Network: Graphic Layout Generation
with Constraints},
year = {2020}
}