This is a collection of repositories for graphic layout generation. Graphic layout is the arrangement of visual elements. Graphic layout generation aims at generating aesthetically pleasing layouts based on diverse user requirements.

We mainly focus on three critical topics in graphic layout generation.

• Capture characteristics of graphic layouts: Coarse-to-Fine and LayoutDiffusion.
• Model user requirements: Parse-then-Place.
• Unify different tasks: LayoutFormer++ and LayoutPrompter.

• LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models. Junyi Zhang, Jiaqi Guo, Shizhao Sun, Jian-Guang Lou, and Dongmei Zhang. ICCV 2023.

• A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptions. Jiawei Lin, Jiaqi Guo, Shizhao Sun, Weijiang Xu, Ting Liu, Jian-Guang Lou, and Dongmei Zhang. ICCV 2023.

• LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction. Zhaoyun Jiang, Jiaqi Guo, Shizhao Sun, Huayu Deng, Zhongkai Wu, Vuksan Mijivic, Zijiang James Yang, Jian-Guang, Lou, and Dongmei Zhang. CVPR 2023.

• Coarse-to-Fine Generative Modeling for Graphic Layouts. Zhaoyun Jiang, Shizhao Sun, Jihua Zhu, Jian-Guang Lou, and Dongmei Zhang. AAAI 2022.

• Aesthetics++: Refining Graphic Designs by Exploring Design Principles and Human Preference. Wenyuan Kong, Zhaoyun Jiang, Shizhao Sun, Zhuoning Guo, Weiwei Cui, Ting Liu, Jian-Guang Lou, and Dongmei Zhang. TVCG 2022.

• Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics. Chunyao Qian, Shizhao Sun, Weiwei Cui, Jian-Guang Lou, Haidong Zhang, and Dongmei Zhang. VIS 2020.

• Unleashing the Potential of AI for Graphic Layout Generation. CVPR AICC Workshop 2023.

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