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}
    }

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