Natural-Language Image Editing With Region-Level AI Control
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Solution Overview
Problem
Existing image editing technologies require user proficiency with image editing tools and often necessitate multiple iterations to achieve desired edits, especially when simple modifications are needed.
Innovation Solution
The use of AI systems, specifically machine learning models like Generative Adversarial Networks (GANs) or diffusion models, to facilitate image editing by receiving natural language instructions and automatically editing specified regions within images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image editing tools are used, then precise editing control is achieved, but user proficiency and complexity increase
Solution Approach 1:
The patent introduces an AI model as an intermediary between the user and the image editing system. The user provides natural language instructions, and the AI model translates these into precise editing operations. This mediator enables non-expert users to achieve professional-grade editing results without learning complex tools, resolving the contradiction between editing precision and ease of operation
Solution Approach 2:
The patent replaces manual mechanical editing operations with an automated AI-based system. Instead of users manually adjusting parameters and tools, the system uses machine learning models to automatically interpret natural language commands and apply appropriate edits, substituting the mechanical interaction model with an intelligent automation model that maintains precision while improving accessibility
2Measurement precision
If manual image editing is performed, then precise control over edits is achieved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-training AI models on extensive image editing datasets and pre-processing input images through analysis and segmentation. When a user provides editing instructions, the model has already established the framework for execution, significantly reducing the time required to perform the actual editing while maintaining precise control over the modifications
Solution Approach 2:
The patent implements self-service through automated editing workflows where the AI model independently analyzes the image, identifies relevant regions, applies appropriate editing operations, and generates results without requiring manual intervention at each step. The system serves itself by autonomously completing the editing task based on high-level user instructions, dramatically reducing time consumption while preserving editing precision through the model's trained capabilities
3Extent of automation
If AI models generate images from text, then automation is improved, but iteration requirements increase
Solution Approach 1:
The patent incorporates feedback mechanisms where the AI model generates initial edits, evaluates them against the user's natural language instructions and the original image context, and automatically refines the results. This feedback loop enables the system to learn from its own outputs and make corrections without requiring multiple manual iteration cycles, maintaining high automation while reducing the time needed to achieve satisfactory results
Data Source
AI summary
In some implementations, the techniques described herein relate to a method including: (i) identifying, by a processor, an image. (ii) receiving, by the processor, natural language instructions for editing the image, the natural language instructions including a location within the image and an editing instruction, (iii) editing, by a machine learning model executed by the processor, the location within the image based on the natural language instructions by (a) identifying a region within the image that corresponds to the location in the natural language instructions and (b) editing the identified region by applying the editing instruction to the identified region to generate an edited image, and (iv) causing, by the processor, display of the edited image.