LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators (1901.06767v1)

Abstract: Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements. The generator of LayoutGAN takes as input a set of randomly-placed 2D graphic elements and uses self-attention modules to refine their labels and geometric parameters jointly to produce a realistic layout. Accurate alignment is critical for good layouts. We thus propose a novel differentiable wireframe rendering layer that maps the generated layout to a wireframe image, upon which a CNN-based discriminator is used to optimize the layouts in image space. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation and tangram graphic design.

• The paper introduces LayoutGAN, a novel GAN that generates structured graphic layouts by modeling precise geometric relationships among discrete elements.
• It employs a wireframe rendering discriminator that transforms layouts into wireframe images, enabling CNN-based evaluation to optimize alignment.
• Experimental results demonstrate improved performance in MNIST digit, document, and tangram design generation, showcasing enhanced coherence and design precision.

Overview of "LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators"

The paper "LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators" introduces a novel approach to graphic layout synthesis using Generative Adversarial Networks (GANs). This approach, termed LayoutGAN, focuses on creating structured layouts by modeling geometric relationships among 2D graphical elements. It seeks to advance the state of graphic design generation, an area traditionally regarded as trailing behind in generative model development due to the complex nature of design which involves vector representations rather than raster images.

Key Contributions

The LayoutGAN framework presents several significant contributions to the domain of generative models for graphic layout design:

1. Structured Data Generation:
   • Unlike traditional GANs that operate in pixel space, LayoutGAN directly synthesizes layouts composed of discrete, labeled graphic elements, represented with class probabilities and geometric parameters.
   • This structured approach allows the generator to produce designs that accurately reflect the intended relational semantics of various layout components.
2. Wireframe Rendering Discriminator:
   • A novel differentiable wireframe rendering layer transforms generated layouts into wireframe images, enabling a convolutional neural network (CNN)-based discriminator to optimize layout alignments effectively. This method enhances the model's sensitivity to geometric arrangements, reducing misalignment and occlusion problems that a relation-based discriminator may encounter.
3. Permutation-Invariant Generator:
   • A unique attribute of the generator is its permutation invariance. It ensures that the output layout remains consistent irrespective of the order in which input elements are presented, underpinning a robust generative process.

Methodology and Architecture

The architecture of LayoutGAN features a permutation-invariant generator composed of self-attention modules for contextual embedding and refinement of graphic elements' features. The innovative discriminator, crucial to the system's efficacy, leverages a wireframe rendering mechanism ensuring a better understanding of visual spatial patterns compared to conventional pixel-based discriminators.

This framework is rigorously tested across diverse application areas, such as MNIST digit generation, document layout generation, and tangram graphic design, successfully replicating realistic and coherent layouts in each case.

Experimental Results

Experimental results demonstrate the practical effectiveness of LayoutGAN:

• MNIST Digit Generation:

The wireframe rendering discriminator achieved a higher inception score compared to the relation-based discriminator, indicating superior model capability in synthesizing coherent and realistic digit layouts.

• Document Layout Generation:

Tests revealed that LayoutGAN can capture diverse layout patterns and minimize alignment errors, outperforming DCGANs that synthesize layouts by learning only from rendered images.

• Clipart Abstract Scene and Tangram Design:

The model adeptly generates complex graphic configurations, with user studies showing a preference for outputs produced by the wireframe rendering discriminator due to enhanced alignment and scene coherence.

Implications and Future Work

The capability of LayoutGAN to manage intrinsic relationships among graphic elements and accurately model human design patterns has implications for automation in graphic design workflows, potentially aiding in tasks such as automated document layout, UI design, and beyond. The shift towards generating vector-like structured data rather than raster images represents a meaningful progression in generative techniques.

Future research can build upon LayoutGAN by exploring extensions that incorporate content representation, such as integrating text and icons directly into the design elements, thereby facilitating more comprehensive automated graphic design solutions. Additionally, expanding the model to handle three-dimensional data could open avenues for more applications in virtual and augmented reality environments.

The LayoutGAN paradigm thus sets a promising foundation for ongoing developments in structured data generation within the context of graphic design, with its innovative discriminator approach and strong baseline results paving the way for further advancements.