Paper in ECCV 2020 on “Neural Design Network: Graphic Layout Generation with Constraints”


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.


Hsin-Ying Lee, Lu Jiang, Irfan Essa, Madison Le, Haifeng Gong, Ming-Hsuan Yang, Weilong Yang

Neural Design Network: Graphic Layout Generation with Constraints Proceedings Article

In: Proceedings of European Conference on Computer Vision (ECCV), 2020.

Links | BibTeX | Tags: computer vision, content creation, ECCV, generative media, google

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