LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer (2212.09877v4)

Abstract: Graphic layout designs play an essential role in visual communication. Yet handcrafting layout designs is skill-demanding, time-consuming, and non-scalable to batch production. Generative models emerge to make design automation scalable but it remains non-trivial to produce designs that comply with designers' multimodal desires, i.e., constrained by background images and driven by foreground content. We propose LayoutDETR that inherits the high quality and realism from generative modeling, while reformulating content-aware requirements as a detection problem: we learn to detect in a background image the reasonable locations, scales, and spatial relations for multimodal foreground elements in a layout. Our solution sets a new state-of-the-art performance for layout generation on public benchmarks and on our newly-curated ad banner dataset. We integrate our solution into a graphical system that facilitates user studies, and show that users prefer our designs over baselines by significant margins. Code, models, dataset, and demos are available at https://github.com/salesforce/LayoutDETR.

• The paper introduces LayoutDETR, which integrates object detection transformers with generative modeling to automate multimodal layout design.
• The method achieves state-of-the-art performance with improved FID scores and enhanced inter-box regularity.
• The practical graphical system, validated by user studies, offers a scalable tool for designers to create efficient, aesthetically pleasing layouts.

LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer

The paper under review introduces LayoutDETR, a novel method for graphic layout design harnessing the synergistic capabilities of generative modeling and detection transformers. LayoutDETR addresses the complexities inherent in automated design of multimodal layouts, striking a balance between satisfying content-aware needs and achieving high-quality, realistic graphic design outputs.

Overview of LayoutDETR

LayoutDETR is designed to automate the design of layouts that incorporate both background images and multimodal foreground elements, which involve combinations of text and images. By adhering to the multilayered nature of graphic design, LayoutDETR innovates by framing layout generation as a detection task. The system is based on the DETR architecture, well-known for its efficiency in object detection via transformers. LayoutDETR leverages this architecture to predict suitable locations and scales for layout elements, ensuring compliance with aesthetic norms and user intentions.

Methodological Contributions

The contributions of LayoutDETR are noteworthy in several critical dimensions:

1. Method Integration: LayoutDETR uniquely bridges the domains of layout generation and object detection. Unlike existing methods, it supports a variety of multimodal inputs without sacrificing coherence in visual design.
2. Data and Evaluation: It introduces a novel ad banner dataset comprised of 7,196 samples, curated to enhance research in multimodal layout designs. Notably, the system achieves state-of-the-art results across several benchmarks, underscoring its practical applicability.
3. System and User Feedback: An integral aspect of this research is the development of a user-centric graphical system, allowing practical implementation and collection of qualitative user feedback. In extensive user studies, LayoutDETR outperformed baseline models, reflecting user preference for its layout designs.

Numerical Results

The paper provides compelling empirical evidence to reinforce its claims. On public benchmarks, as well as the newly-curated dataset, LayoutDETR demonstrates superior performance through quantitative metrics:

• It achieves statistically significant improvements in Fréchet Inception Distance (FID) for both layout and rendered images.
• The generated layouts exhibit enhanced inter-box regularity and superior alignment properties.
• User preferences collected through a systematic paper affirm that LayoutDETR's outputs are favored over those of competing models by significant margins.

Practical and Theoretical Implications

LayoutDETR has profound implications in both the theoretical understanding and practical application of AI in graphic design:

• Theoretical Implications: It underscores the potential of integrating detection transformers with generative models, opening avenues for further exploration in multimodal conditional generation tasks. The Detector-GAN/DETR hybrid architecture introduced could inform future design of visual understanding systems.
• Practical Implications: The graphical system developed offers a robust tool for designers, presenting a scalable alternative to manual layout design. Its deployment could revolutionize industries dependent on large-scale media production, enabling rapid yet aesthetically rich content creation.

Future Directions

As a pioneering work, LayoutDETR lays the foundation for numerous emerging directions:

• Enhanced Multimodal Integration: Future iterations could include broader categories of data and even more granular control over layout parameters.
• Scalability and Customization: Addressing scalability to industrial levels while allowing for more customization in user preferences or domain-specific design rules.
• Incorporation of Reinforcement Learning: Use RL techniques to iteratively improve the quality of layout design by interacting with complex user feedback in real-time systems.

In conclusion, LayoutDETR represents a significant advancement in the automated design of graphic layouts, merging the strengths of detection transformers and generative models. Its innovative approach and demonstrated effectiveness not only extend the capabilities of current AI design systems but also prompt further inquiry into multimodal design frameworks.