Neural Design Network: Graphic Layout Generation with Constraints
Hsin-Ying Lee, Lu Jiang, Irfan Essa, Phuong B Le, Haifeng Gong, Ming-Hsuan Yang, Weilong Yang
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."
BibTeX
@inproceedings{eccv2020_neuraldesignnetw,
title = {Neural Design Network: Graphic Layout Generation with Constraints},
author = {Hsin-Ying Lee and Lu Jiang and Irfan Essa and Phuong B Le and Haifeng Gong and Ming-Hsuan Yang and Weilong Yang},
booktitle = {ECCV 2020},
year = {2020}
}