Image Generation via Content Code Structural Encoding
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Solution Overview
Problem
Existing AI-based image generation technologies struggle with properly generating image details, altering existing structural information, and degrading image quality due to lack of information or bias towards specific dataset domains, leading to unreliable and limited usability.
Innovation Solution
A method involving a content encoder and a decoder, part of neural network models, to generate and transform content codes between different domain styles, ensuring structural information integrity and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI-based image generation technology is used to generate images, then new images can be created based on text inputs, but image details are not properly generated and structural information is changed or damaged
Solution Approach 1:
The patent introduces an intermediary representation (latent code or feature map) that captures structural information from the input image. This intermediary serves as a bridge between the text prompt and the generated image, ensuring that structural constraints are preserved while allowing creative generation. The intermediary encoding the structural information guides the generation process to maintain accuracy.
Solution Approach 2:
The patent segments the image generation process into distinct components: one handling structural information preservation and another handling creative content generation based on text inputs. By dividing the generation task into separate functional modules, each can be optimized independently - one for maintaining structural fidelity and another for adapting to text descriptions.
2Productivity
If existing image generation models are used, then images can be generated quickly, but image quality degrades due to lack of information or bias toward specific dataset domains
Solution Approach 1:
The patent performs preliminary encoding of structural information from the input image before the main generation process. This preliminary action extracts and stores essential structural features that will be referenced during generation, ensuring that quality-critical information is preserved upfront. This allows the generation process to proceed quickly while maintaining access to high-quality structural constraints.
Solution Approach 2:
The patent changes the parameter representation by introducing a specialized latent space or feature encoding that captures domain-invariant structural information. By transforming the representation parameters to focus on essential structural characteristics rather than domain-specific details, the model generalizes better across different domains while maintaining consistent quality.
3Manufacturing precision
If image generation technology processes complex structural information, then detailed images can be created, but the structural information is altered or damaged during generation
Solution Approach 1:
The patent implements a feedback mechanism where the encoded structural information is continuously referenced during the generation process. The structural constraints are fed back into the generation model at multiple stages, ensuring that even as detailed content is generated, the structural integrity is maintained through continuous reference to the original structural encoding.
Solution Approach 2:
The patent uses an intermediary structural encoding that acts as a stable reference throughout the generation process. This intermediary representation preserves the essential structural information in a form that can guide detailed generation without being directly modified, thus maintaining structural integrity while enabling high-detail output.
Data Source
AI summary
Provided is a method for generating an image, which is performed by one or more processors and which includes receiving a first image, generating, using a content encoder, a first content code associated with the first image, generating, using a decoder, a second image based on the generated first content code, and outputting the generated second image.