AI Image Generator for Contextual Product Visualization
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Solution Overview
Problem
Current systems for custom product visualization in online shopping lack the ability to generate synthetic backgrounds and images in real-time, matching the customized product, leading to delayed user interactions and an unenhanced user experience due to reliance on pre-prepared stock images that often do not relate to the actual product being customized.
Innovation Solution
The implementation of an AI-based image generation approach using Stable Diffusion (SD) technology to create on-the-fly synthetic backgrounds and images, driven by textual queries that incorporate product descriptions, user profiles, and customization parameters, allowing for dynamic and context-specific visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If stock images are used as backgrounds for displaying customized products, then the system complexity is reduced and images are readily available, but the user experience is not enhanced and the images are rarely related to the actual product being customized
Solution Approach 1:
The system uses product parameters (color, material, type) as inputs to generate corresponding background images. By changing the input parameters based on the customized product specifications, the system dynamically generates relevant backgrounds that match the product being displayed, resolving the contradiction between background relevance and system complexity
Solution Approach 2:
An AI-based image generation model serves as an intermediary between the product customization data and the background display. This mediator translates product parameters into contextually relevant background images, enabling adaptive backgrounds without requiring a complex manual curation system
2Adaptability or versatility
If synthetic backgrounds are generated in real-time based on product customization information, then user experience is enhanced and images are context-specific, but the processing time increases and latency is introduced
Solution Approach 1:
The system pre-loads and caches base background images and product asset libraries before the customization process begins. This preliminary preparation allows the real-time generation to focus only on combining and rendering specific elements, significantly reducing the actual generation latency while maintaining context-specific customization
Solution Approach 2:
The image generation process is segmented into independent components: base background selection, product asset rendering, and composite assembly. This segmentation allows parallel processing of different elements and enables caching of frequently used components, reducing overall processing time while maintaining customization quality
3Manufacturing precision
If AI-based image generation is implemented to create synthetic backgrounds on-the-fly, then image quality and relevance are improved, but the computational resources and processing power required increase
Solution Approach 1:
The system generates only the necessary portions of background images required for each specific customization scenario, rather than generating complete high-resolution images from scratch. This partial generation approach maintains sufficient visual quality for the application while significantly reducing computational energy consumption
Data Source
AI summary
In some embodiments, a method for using a custom product as a key for context-based image generation comprises: receiving, by a visualization service of a computer collaboration system, a graphical representation of a custom digital product; determining one or more regions for customizing regions; for a region of the regions: determining a set of phrases that are specific to the custom digital product and a customization process for customizing the custom digital product; based on the set of phrases, generating a synthetic image generation request; transmitting request to an AI based image generator configured to generate synthetic images; causing the AI-based generator to: generate a customized synthetic image based on the request and a repository of images; wherein the customized synthetic image is not a copy of any image from the repository of images; transmit the customized synthetic image to the computer collaboration system; including the image in the region; causing displaying the graphical visualization of the custom digital product along with the customized synthetic image in the region.