Vision-Language Image Editing from Fuzzy Themes
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Solution Overview
Problem
Existing image editing methods require specific executable instructions for precise operations, failing to accommodate fuzzy editing themes, leading to poor user experience for those without professional editing skills or clear expectations.
Innovation Solution
An image editing method using a preset vision-language model to generate editing instructions and positions based on a fuzzy editing theme, enabling creative and flexible editing by predicting positions and generating diverse suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If specific executable instructions are provided for image editing, then editing precision is improved, but ease of operation deteriorates because users need professional editing skills and clear expectations
Solution Approach 1:
The patent introduces an intermediary system comprising a vision-language model and an editing system that translates fuzzy natural language themes into precise editing instructions. The vision-language model acts as a mediator between the user's intent and the editing execution, automatically generating executable instructions with position information, thereby eliminating the need for users to provide precise manual instructions while maintaining high editing precision.
2Manufacturing precision
If existing image editing methods are used, then editing precision is improved, but adaptability deteriorates because they cannot handle fuzzy editing themes
Solution Approach 1:
The patent changes the parameter of editing input from precise but rigid instructions to fuzzy but flexible natural language themes. The vision-language model processes these thematic descriptions and dynamically generates appropriate editing instructions, allowing the system to adapt to various editing scenarios and user intents while maintaining precise execution through automated instruction generation.
3Ease of operation
If natural language instructions are accepted, then ease of operation is improved, but manufacturing precision deteriorates because instructions may not be executable or specific enough
Solution Approach 1:
The patent replaces the mechanical system of manual instruction formulation with an automated vision-language model that generates executable editing instructions. Instead of requiring users to manually craft precise instructions, the system uses AI to translate natural language themes into structured editing commands with position information, substituting human cognitive effort with automated processing while ensuring instruction precision.
Data Source
AI summary
Embodiments of the present disclosure disclose an image editing method and apparatus, an electronic device, and a storage medium. The method includes: receiving an image to be edited and an editing theme; generating, by using a preset vision-language model, an editing instruction and an editing position corresponding to the editing instruction based on the image to be edited and the editing theme; and editing the image to be edited based on the editing instruction and the editing position, to obtain a target image.