Content-Conditioned Layout Generation for Semantic Graphic Design
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Solution Overview
Problem
Conventional graphic design systems fail to accurately generate layouts that reflect semantic considerations and multimodal parameters, leading to inaccurate and inefficient content placement and sizing, and lack controllable content conditioning.
Innovation Solution
A content-conditioned variational generative model is employed to generate graphic design layouts by training a variational autoencoder and a conditioning function, using content conditions such as images, keywords, and ratios to accurately place and size text and images based on semantic aspects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional graphic design systems use pre-generated design templates, then users can quickly access design layouts, but the systems fail to accurately generate layouts that reflect semantic considerations and multimodal parameters
Solution Approach 1:
The patent transforms the layout generation process from template selection to parameter-driven generation. The system uses a trained generative model that takes semantic parameters (text content, image content, keywords) and multimodal parameters (text ratio, image ratio, category) as inputs to generate layout parameters (text box positions, image positions, sizing). This parameter transformation enables accurate layout generation that adapts to different semantic and multimodal conditions.
Solution Approach 2:
The patent replaces the mechanical template selection system with an intelligent generative model. Instead of manually selecting from pre-generated templates, the system uses a trained neural network model (LayoutNet or LayoutDETR) that automatically generates optimal layouts based on semantic and multimodal inputs. This substitution enables the system to understand semantics and generate accurate, adaptive layouts without relying on fixed templates.
2Productivity
If conventional systems provide template selection functionality, then users can choose from existing designs, but the systems require excessive device interactions and lack efficiency
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate layouts without requiring users to manually select templates or adjust parameters. The trained generative model takes semantic and multimodal inputs and autonomously produces optimized layout parameters, eliminating the need for excessive user interactions with template selection interfaces and significantly improving generation efficiency.
Solution Approach 2:
The patent applies preliminary action by pre-training the generative model on large datasets of design layouts before deployment. The model learns optimal layout patterns and relationships between semantic/multimodal parameters and layout configurations in advance. When generating new layouts, the pre-trained model can immediately produce accurate results without requiring users to manually explore templates or make iterative adjustments.
3Adaptability or versatility
If conventional systems use fixed template layouts, then design consistency is maintained, but the systems lack controllable content conditioning
Solution Approach 1:
The patent introduces dynamics by transforming fixed template layouts into dynamic, adaptive layouts. The system uses a trained generative model that continuously adjusts layout parameters based on the specific semantic and multimodal content being processed. The model dynamically determines text box positions, image positions, sizing, and other layout characteristics, enabling precise content conditioning control while maintaining design consistency through learned patterns from training data.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating graphic design layouts using a machine learning model to modify a layout according to content conditions. For example, the disclosed systems receive, from a client device, one or more content conditions defining parameters for generating design layouts. In some embodiments, the disclosed systems generate a set of fused features from the one or more content conditions. In certain embodiments, the disclosed systems encode, utilizing a machine learning, a content-conditioned layout embedding from the set of fused features. In some embodiments, the disclosed systems generate, from the content-conditioned layout embedding utilizing the machine learning model, a design layout comprising layout parameters defined by the one or more content conditions.