Scene-Based Image Editing Using Semantic Object Masks
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to edit digital images at the pixel level, which is cumbersome and often requires deep knowledge of the system and its tools, leading to a non-intuitive and time-consuming editing process.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to process digital images as if they were real scenes, pre-processing and anticipating user interactions by generating object masks and content fills, allowing for intuitive editing of semantic areas rather than individual pixels, thereby reducing user input and simplifying the editing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems edit digital images at the pixel level, then editing precision is maintained, but ease of operation deteriorates and user interaction complexity increases
Solution Approach 1:
The system segments the digital image into multiple semantic objects using machine learning models, allowing users to interact with high-level object concepts rather than individual pixels. This segmentation transforms the complex pixel-level editing task into simpler object-level operations, resolving the contradiction between ease of operation and operation complexity.
Solution Approach 2:
The system introduces semantic object masks and content fills as intermediary representations between the user and the pixel data. Users interact with these semantic intermediaries (object selections, attribute modifiers) rather than directly manipulating pixels, which simplifies the user interface while maintaining precise editing capabilities through the intermediary's translation to pixel-level changes.
2Productivity
If conventional image editing systems require significant user interaction, then editing control is maintained, but productivity deteriorates and time consumption increases
Solution Approach 1:
The system performs preliminary actions by automatically generating object masks, identifying semantic objects, and preparing content fills before the user actually requests editing. This pre-processing work is done in advance using machine learning models, so when users want to edit, the heavy lifting has already been completed, significantly improving productivity and reducing time consumption.
Solution Approach 2:
The system provides self-service by automatically performing image analysis, object segmentation, and content generation tasks without requiring user intervention. The machine learning models autonomously understand the image content and prepare editing options, allowing users to simply make high-level decisions rather than manually performing tedious preprocessing steps.
3Manufacturing precision
If conventional image editing systems operate at pixel level, then manufacturing precision is maintained, but ease of operation deteriorates
Solution Approach 1:
The system adds a semantic dimension to the traditional pixel-level editing paradigm. Instead of operating only in the pixel coordinate space, users can now operate in a semantic object space where images are composed of meaningful entities. This additional dimension allows precise editing through high-level concepts while maintaining the underlying pixel-level precision through the semantic-to-pixel translation layer.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems detect a selection of an object portrayed in a digital image displayed within a graphical user interface of a client device. The disclosed systems provide, for display within the graphical user interface in response to detecting the selection of the object, an interactive window displaying one or more attributes of the object. The disclosed systems receive, via the interactive window, a user interaction to change an attribute from the one or more attributes. The disclosed systems modify the digital image by changing the attribute of the object in accordance with the user interaction.