Composite Image Rendering with Alpha Layers for Editable Objects
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Solution Overview
Problem
Current generative AI systems fail to produce images that are digitally formatted for use in computer-based graphic design implementations, such as video game creation, and lack the ability to decompose images so that individual aspects can be changed independently.
Innovation Solution
An apparatus and method that utilize a text-to-image model to render graphical objects with alpha transparency, allowing for independent manipulation and editing of image components as separate layers, enabling fine-grained control over graphic design elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current generative AI systems re-work an entire generative image responsive to a prompt, then the image can be updated with new content, but the entire image must be regenerated even when only certain aspects need changing
Solution Approach 1:
The patent segments the generative image into multiple independent graphical objects (foreground objects, background objects, midground objects) that can be individually manipulated. This allows only the necessary portions of the image to be regenerated when a prompt is received, rather than regenerating the entire image. The segmentation enables partial image regeneration by identifying and isolating specific graphical objects that need modification while preserving others.
2Ease of operation
If current generative AI systems generate complete images, then the images are ready for display, but they cannot be decomposed for independent manipulation of individual aspects
Solution Approach 1:
The patent divides the complete image into separate graphical objects with distinct properties (alpha transparency, position, size). Each graphical object can be independently manipulated, moved, or regenerated. This segmentation provides ease of operation by allowing users to modify individual aspects without affecting the entire image, while the system manages the complexity through structured data organization.
Solution Approach 2:
The patent utilizes alpha transparency (a form of visual property modification) to distinguish different graphical objects and enable their independent manipulation. By assigning different alpha values and transparency properties to various graphical objects, the system enables selective editing and layering while maintaining the overall image structure.
3Adaptability or versatility
If current generative AI systems produce formatted images, then the images are ready for use, but they are not digitally formatted for use in computer-based graphic design implementations
Solution Approach 1:
The patent generates graphical objects with universal properties (alpha transparency, positional coordinates, scalable dimensions) that make them compatible with various computer-based graphic design implementations, including video game creation. The output format is designed to be universally applicable across different platforms and applications, allowing the same graphical objects to be used in multiple contexts without requiring conversion or reformatting.
Data Source
AI summary
In one aspect, graphic design can be accomplished through generative, object-based composite image rendering. Additionally, alpha transparency can be included in the generative images. Thus, text-to-image models may be used to independently configure the appearance of different graphical objects that are presented in the same graphics space, with primitive base images being used as templates and with the text-to-image model using the templates and a prompt to then generate additional graphical objects with alpha transparency. The generated graphical objects can then act as layers with respect to each other such that they can be independently exported, moved, and further adjusted via additional prompts to the text-to-image model.