AI Image Editing via Natural Language Inference

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Solution Overview

Problem

Current image editing tools require users to manually understand and manipulate various aspects of images, such as color, hue, and saturation, making complex editing tasks like localized image editing cumbersome and time-consuming, especially when using natural language processing tools that offer limited functionality.

Innovation Solution

An artificial intelligence model comprising an operation classifier, a grounding model, and an operation modular network infers local or global image editing operations from natural language requests, automatically identifying relevant areas and performing editing operations with inferred parameters on a source image.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If users manually edit images using traditional image editing tools, then they can perform precise image editing operations, but the process becomes complicated, effort-intensive, and time-consuming

Engineering Contradiction:

Improveimage editing precisionVSAvoidimage editing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system enables self-service image editing by automatically inferring editing operations and parameters from natural language requests. The AI model performs operations like brightness adjustment, color correction, and object removal without requiring users to manually configure technical parameters, thus reducing time consumption while maintaining editing precision.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

A natural language processing intermediary is introduced between the user and the image editing system. Users provide simple text descriptions, and the AI model translates these into precise editing operations, bridging the gap between user intent and technical execution without requiring users to understand complex image editing parameters.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If users use natural language processing tools for image editing, then the ease of operation improves, but the functionality remains limited and cannot perform complex localized editing tasks

Engineering Contradiction:

Improveimage editing easeVSAvoidediting functionality

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system segments the image editing process into distinct operational modules (e.g., brightness adjustment, color correction, object removal, background replacement). Each module can be independently activated based on the natural language request, enabling versatile functionality while maintaining ease of operation through simple text commands.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adapts its functionality based on the user's natural language request. The AI model analyzes the request context and automatically selects and configures appropriate editing operations, allowing the system to perform both simple and complex editing tasks without requiring users to specify technical details, thus enhancing versatility while preserving ease of operation.

Inventive Principle:

Principle #15Dynamics

3Ease of operation

If natural language processing tools are used for image editing, then the ease of operation improves, but the ability to automatically perform localized image editing is lost

Engineering Contradiction:

Improveimage editing easeVSAvoidlocalized editing automation

Core Design Contradiction:

Ease of operationVSExtent of automation

Solution Approach 1:

The system performs preliminary actions by automatically identifying and segmenting target regions in the image before applying editing operations. The AI model analyzes the natural language request, locates relevant objects or areas, and prepares localized editing masks in advance, enabling automatic localized editing while maintaining ease of operation through simple text commands.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual mechanical selection processes with AI-based automatic region identification. Instead of requiring users to manually select areas for editing, the AI model automatically detects and segments target regions based on the natural language request, substituting manual operations with intelligent automation while preserving ease of use.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11670023B2Artificial intelligence techniques for performing image editing operations inferred from natural language requests

Publication Date: 2023.06.06 ADOBE INC

AI summary

This disclosure involves executing artificial intelligence models that infer image editing operations from natural language requests spoken by a user. Further, this disclosure performs the inferred image editing operations using inferred parameters for the image editing operations. Systems and methods may be provided that infer one or more image editing operations from a natural language request associated with a source image, locate areas of the source that are relevant to the one or more image editing operations to generate image masks, and performing the one or more image editing operations to generate a modified source image.