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.
|Title of host publication||Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings|
|Editors||Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||16|
|Publication status||Published - 2020|
|Event||16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom|
Duration: 2020 Aug 23 → 2020 Aug 28
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||16th European Conference on Computer Vision, ECCV 2020|
|Period||20/8/23 → 20/8/28|
Bibliographical noteFunding Information:
This work is supported in part by the NSF CAREER Grant #1149783.
© 2020, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)