Multimodal Publication Layout Generation for Scalable Design Quality
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Solution Overview
Problem
Traditional graphic layout design for digital publications is time-consuming and costly, limiting scalability and efficiency in batch production, as it requires human expertise and is not easily automated.
Innovation Solution
A multimodal conditioned graphic layout generation system that uses a generative model trained with an encoder, generative adversarial network, and conditional reconstructor to automatically generate layouts based on background and foreground elements, leveraging neural networks for efficient and accurate layout design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human designers manually create graphic layout designs, then design quality and expertise are maintained, but production time and cost increase significantly
Solution Approach 1:
The system enables layouts to generate themselves automatically through machine learning models. The layout generation module takes content elements as input and autonomously produces optimized layouts without human intervention, allowing the system to serve itself in the design creation process.
Solution Approach 2:
The patent replaces the mechanical human design process with an automated machine learning system. The neural network models process content elements and generate layouts computationally, substituting human manual work with algorithmic automation while maintaining or improving design quality.
2Manufacturing precision
If human designers manually create graphic layout designs, then design expertise is applied, but scalability to batch production is limited
Solution Approach 1:
The layout generation system is designed to handle multiple types of content elements (text, images, videos) and generate various layout styles universally. The same machine learning model can process different content inputs and produce appropriate layouts for diverse publication types, enabling scalable batch production across multiple contexts.
Solution Approach 2:
The system autonomously generates layouts for batch production without requiring human designers for each individual case. The automated pipeline processes multiple content inputs sequentially or in parallel, scaling production capacity while maintaining consistent design quality through the trained machine learning models.
3Productivity
If automated systems are used for layout generation, then production efficiency increases, but design quality and contextual appropriateness may deteriorate
Solution Approach 1:
The system performs preliminary training on large datasets of high-quality layouts before actual generation. The machine learning models are pre-trained to learn design principles, aesthetic rules, and contextual relationships from extensive training data, enabling them to produce quality outputs automatically during deployment without sacrificing design standards.
Solution Approach 2:
The system incorporates feedback mechanisms where generated layouts are evaluated and used to refine the models. The training process uses feedback from training data and performance metrics to continuously improve generation quality, ensuring that automated outputs meet or exceed human-designed quality standards while maintaining high production efficiency.
Data Source
AI summary
Embodiments described herein provide systems and methods for multimodal layout generations for digital publications. The system may receive as inputs, a background image, one or more foreground texts, and one or more foreground images. Feature representations of the background image may be generated. The foreground inputs may be input to a layout generator which has cross attention to the background image feature representations in order to generate a layout comprising of bounding box parameters for each input item. A composite layout may be generated based on the inputs and generated bounding boxes. The resulting composite layout may then be displayed on a user interface.