AI Image Output Alignment Across Models With Match Scoring
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Solution Overview
Problem
Existing systems struggle to efficiently align and match outputs from different AI-based image generation systems, particularly when generating graphical images for various genres, leading to inefficiencies in creating multiple versions of virtual objects and scenes for diverse graphics modes in video games.
Innovation Solution
A system and method that utilizes an input processor, multiple AI-based image generation systems, an image matching assessment system, and specification adjustment engines to iteratively refine trial images until they meet a minimum similarity score with a reference image, leveraging AI models for automatic alignment and adjustment of input specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple AI-based image generation systems are used to create different graphics modes, then the versatility and quality of graphical content are improved, but the complexity of aligning and matching outputs from different systems increases
Solution Approach 1:
The patent introduces an image matching assessment system as an intermediary component that mediates between multiple AI-based image generation systems. This assessment system processes images from different AI systems and evaluates their similarity, providing a standardized mechanism to bridge the complexity of aligning outputs from diverse generation systems while maintaining versatility across graphics modes.
Solution Approach 2:
The patent employs parameter changes by adjusting input specifications iteratively to improve image match scores. The system modifies parameters such as prompt descriptions, generation settings, and other controllable variables to align outputs from different AI systems, thereby managing the complexity of multi-system integration through systematic parameter optimization.
2Manufacturing precision
If iterative refinement processes are used to improve image similarity, then the manufacturing precision of graphical content is improved, but the productivity and time-to-market are reduced
Solution Approach 1:
The patent implements a feedback mechanism where the image matching assessment system continuously evaluates the similarity between reference images and generated images, and this feedback is used to iteratively adjust input specifications. This closed-loop feedback process automates the refinement procedure, improving image similarity precision while reducing manual intervention time and enhancing overall productivity.
Solution Approach 2:
The system enables self-service by automatically performing iterative refinement without requiring constant manual input from designers. The specification adjustment engine autonomously modifies parameters based on assessment feedback, allowing the system to self-correct and self-optimize image similarity, thereby maintaining high precision while improving productivity.
3Ease of operation
If automated specification adjustment is implemented, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The patent extracts the complex task of specification adjustment from manual designer operations and transfers it to an automated specification adjustment engine. By taking out this complex functionality and encapsulating it in a dedicated automated component, the system improves ease of operation for users while managing the inherent complexity through modular system architecture.
Data Source
AI summary
A first AI-based image generation system generates a reference image based on a reference input specification that is initially set equal to an initial input for AI-based image generation. A second AI-based image generation system generates a trial image based on a trial input specification that is initially set equal to the initial input for AI-based image generation. An image matching assessment system processes the trial image and the reference image through an image analysis AI model to determine an amount of similarity therebetween and generate a corresponding image match score. A trial input specification adjustment engine generates a revised version of the trial input specification for the second AI-based image generation system to generate a new trial image that improves the image match score. In this manner, trial images are iteratively generated until the image match score is equal to or greater than the minimum required image match score.