title: From Elements to Design: A Layered Approach for Automatic Graphic Design
    Composition
summary: In this work, we investigate automatic design composition from multimodal
  graphic elements. Although recent studies have developed various generative
  models for graphic design, they usually face the following limitations: they
  only focus on certain subtasks and are far from achieving the design
  composition task; they do not consider the hierarchical information of graphic
  designs during the generation process. To tackle these issues, we introduce the
  layered design principle into Large Multimodal Models (LMMs) and propose a
  novel approach, called LaDeCo, to accomplish this challenging task.
  Specifically, LaDeCo first performs layer planning for a given element set,
  dividing the input elements into different semantic layers according to their
  contents. Based on the planning results, it subsequently predicts element
  attributes that control the design composition in a layer-wise manner, and
  includes the rendered image of previously generated layers into the context.
  With this insightful design, LaDeCo decomposes the difficult task into smaller
  manageable steps, making the generation process smoother and clearer. The
  experimental results demonstrate the effectiveness of LaDeCo in design
  composition. Furthermore, we show that LaDeCo enables some interesting
  applications in graphic design, such as resolution adjustment, element filling,
  design variation, etc. In addition, it even outperforms the specialized models
  in some design subtasks without any task-specific training.
paper_authors._id: 67721e8cb9d0358385f6628d
paper_authors.name: Jiawei Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:54:15.360Z
paper_authors._id: 6440dda9cea37249a0f9b473
paper_authors.avatarUrl: /avatars/9747e1ca11ed5725b2b9968f028cac93.svg
paper_authors.fullname: Jiawei Lin
paper_authors.type: user
paper_authors.user: KyleLin
paper_authors._id: 67721e8cb9d0358385f6628e
paper_authors.name: Shizhao Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:54:08.910Z
paper_authors._id: 63eb00a191a1b8ec4fbba2a9
paper_authors.avatarUrl: /avatars/0cc7cf9b6d05337603f700e0d592edf5.svg
paper_authors.fullname: ShizhaoSun
paper_authors.type: user
paper_authors.user: ShizhaoSun
paper_authors._id: 67721e8cb9d0358385f6628f
paper_authors.name: Danqing Huang
paper_authors._id: 67721e8cb9d0358385f66290
paper_authors.name: Ting Liu
paper_authors._id: 67721e8cb9d0358385f66291
paper_authors.name: Ji Li
paper_authors._id: 67721e8cb9d0358385f66292
paper_authors.name: Jiang Bian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:53:33.295Z
paper_authors._id: 63f253f8f4e30ffd2bd308fb
paper_authors.avatarUrl: /avatars/303f4c7ee588f638acf78a7966786e1e.svg
paper_authors.fullname: Jiang Bian
paper_authors.type: user
paper_authors.user: bianjiang
paper_title: From Elements to Design: A Layered Approach for Automatic Graphic Design
    Composition
paper_summary: In this work, we investigate automatic design composition from multimodal
  graphic elements. Although recent studies have developed various generative
  models for graphic design, they usually face the following limitations: they
  only focus on certain subtasks and are far from achieving the design
  composition task; they do not consider the hierarchical information of graphic
  designs during the generation process. To tackle these issues, we introduce the
  layered design principle into Large Multimodal Models (LMMs) and propose a
  novel approach, called LaDeCo, to accomplish this challenging task.
  Specifically, LaDeCo first performs layer planning for a given element set,
  dividing the input elements into different semantic layers according to their
  contents. Based on the planning results, it subsequently predicts element
  attributes that control the design composition in a layer-wise manner, and
  includes the rendered image of previously generated layers into the context.
  With this insightful design, LaDeCo decomposes the difficult task into smaller
  manageable steps, making the generation process smoother and clearer. The
  experimental results demonstrate the effectiveness of LaDeCo in design
  composition. Furthermore, we show that LaDeCo enables some interesting
  applications in graphic design, such as resolution adjustment, element filling,
  design variation, etc. In addition, it even outperforms the specialized models
  in some design subtasks without any task-specific training.
paper_ai_summary: LaDeCo, a novel approach, uses layered design principles in large multimodal models to perform automatic graphic design composition by breaking down tasks into manageable steps.
paper_ai_keywords: Large Multimodal Models LMMs LaDeCo layer planning semantic layers element attributes design composition layered design principle element filling design variation