Contextual Image Resizing via AI Generation
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Solution Overview
Problem
Current systems for product customization lack efficient methods for generating and rendering high-quality, custom digital images in real-time, limiting user experience and product visualization.
Innovation Solution
The use of AI-based tools and techniques, such as neural networks and stable diffusion models, to generate synthetic images on-the-fly, allowing for real-time customization of product visuals based on user input and product characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If stock images are used as backgrounds for displaying custom products, then the system can provide pre-prepared backgrounds, but users cannot modify or customize them and the user experience is not enhanced
Solution Approach 1:
The system enables users to customize backgrounds by allowing them to upload their own images or select from templates, and automatically generates synthetic product visualizations using their custom backgrounds. This transforms the background from a static pre-prepared element into a user-configurable component that adapts to individual preferences.
Solution Approach 2:
The background transitions from a fixed stock image to a dynamic, user-modifiable element. The system provides real-time preview and adjustment capabilities, allowing users to change backgrounds on-the-fly during the customization process, making the background adaptable rather than static.
2Manufacturing precision
If synthetic images are generated in real-time based on customization information, then high-quality custom images are produced, but significant time delay is introduced
Solution Approach 1:
The system pre-generates multiple background templates and synthetic image variations in advance, storing them for quick retrieval. When a user requests customization, the system selects from pre-prepared options rather than generating images from scratch, significantly reducing generation time while maintaining quality.
Solution Approach 2:
The system generates synthetic images with focused detail only in critical areas (such as product boundaries, shadows, and reflections) while using simpler rendering for background regions. This selective quality approach maintains overall image quality while reducing computational time and resources required.
3Productivity
If AI-based tools generate synthetic images on-the-fly, then real-time customization is enabled, but the complexity of the system increases
Solution Approach 1:
The system introduces an intermediary layer between user input and final image generation, using pre-trained AI models and template libraries as intermediaries. This intermediary layer handles the complex image synthesis tasks using established algorithms rather than requiring complex real-time generation from scratch, simplifying the overall system architecture.
Solution Approach 2:
The image generation process is segmented into independent modules: background selection, product rendering, shadow generation, and composition. Each module handles a specific task using optimized algorithms, allowing parallel processing and reducing overall system complexity compared to a monolithic generation system.
Data Source
AI summary
In some embodiments, a method for generating digital image extensions contextually based on a custom image comprises: determining one or more regions that are to be outside of a custom image once the custom image is applied to a custom digital product; for each region of the one or more regions: determining a request comprising image encoding information of the custom image and information about a customization process for customizing the custom digital product; causing an artificial intelligence (AI) based image generator to generate, based on, the image encoding information of the custom image and the information about the customization process for customizing the custom digital product, a customized synthetic image; including the customized synthetic image in the region; causing displaying, on a display device, a graphical visualization of the synthetic image in the region.