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

doi:10.1007/978-3-030-58580-8_29

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

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Citations (Scopus)

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.

Original language	English
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
Pages	491-506
Number of pages	16
ISBN (Print)	9783030585792
DOIs	https://doi.org/10.1007/978-3-030-58580-8_29
Publication status	Published - 2020
Event	16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 2020 Aug 23 → 2020 Aug 28

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12348 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	20/8/23 → 20/8/28

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-58580-8_29

Cite this

Lee, H. Y., Jiang, L., Essa, I., Le, P. B., Gong, H., Yang, M. H., & Yang, W. (2020). Neural Design Network: Graphic Layout Generation with Constraints. In A. Vedaldi, H. Bischof, T. Brox, & J-M. Frahm (Eds.), Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings (pp. 491-506). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12348 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58580-8_29

Lee, Hsin Ying ; Jiang, Lu ; Essa, Irfan et al. / Neural Design Network : Graphic Layout Generation with Constraints. Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. editor / Andrea Vedaldi ; Horst Bischof ; Thomas Brox ; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. pp. 491-506 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "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.",

author = "Lee, {Hsin Ying} and Lu Jiang and Irfan Essa and Le, {Phuong B.} and Haifeng Gong and Yang, {Ming Hsuan} and Weilong Yang",

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Lee, HY, Jiang, L, Essa, I, Le, PB, Gong, H, Yang, MH & Yang, W 2020, Neural Design Network: Graphic Layout Generation with Constraints. in A Vedaldi, H Bischof, T Brox & J-M Frahm (eds), Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12348 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 491-506, 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 20/8/23. https://doi.org/10.1007/978-3-030-58580-8_29

Neural Design Network: Graphic Layout Generation with Constraints. / Lee, Hsin Ying; Jiang, Lu; Essa, Irfan et al.
Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. ed. / Andrea Vedaldi; Horst Bischof; Thomas Brox; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. p. 491-506 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12348 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Neural Design Network

T2 - 16th European Conference on Computer Vision, ECCV 2020

AU - Lee, Hsin Ying

AU - Jiang, Lu

AU - Essa, Irfan

AU - Le, Phuong B.

AU - Gong, Haifeng

AU - Yang, Ming Hsuan

AU - Yang, Weilong

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings

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PB - Springer Science and Business Media Deutschland GmbH

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ER -

Lee HY, Jiang L, Essa I, Le PB, Gong H, Yang MH et al. Neural Design Network: Graphic Layout Generation with Constraints. In Vedaldi A, Bischof H, Brox T, Frahm J-M, editors, Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 491-506. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-58580-8_29

Neural Design Network: Graphic Layout Generation with Constraints

Abstract

Publication series

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Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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