Perspective-Aware Object Move for Semantic Image Editing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction and specialized knowledge to edit digital images, as they operate on a pixel level rather than recognizing and interacting with semantic areas like objects.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, identifying objects, generating masks and content fills, and creating semantic scene graphs to facilitate intuitive and efficient editing by treating objects as cohesive units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems operate on pixel level, then they can perform detailed image manipulation, but they require significant user interaction and specialized knowledge
Solution Approach 1:
The patent introduces an intermediary layer between the user and the pixel-level editing operations. This intermediary consists of semantic object masks that allow users to select and edit entire objects (like people, cars, buildings) as cohesive units rather than individual pixels. The system automatically generates these masks using machine learning models, bridging the gap between simple user intent and complex pixel manipulation requirements.
Solution Approach 2:
The system performs self-service by automatically generating object masks and understanding image semantics without requiring user intervention. The machine learning models autonomously identify objects, generate corresponding masks, and enable editing operations, freeing users from the need to manually select pixels or understand complex editing parameters.
2Productivity
If machine learning models pre-process images to identify objects and generate masks, then editing efficiency improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images to generate object masks and semantic understanding before the actual editing operation. This upfront work includes running machine learning models to identify objects, create masks, and build scene graphs, so that when users want to edit, the heavy lifting is already done and they can simply select and modify objects.
Solution Approach 2:
The system creates copies of the image data in the form of object masks and semantic representations. These masks are lightweight copies that capture the essential structure and semantics of the image, allowing rapid editing operations without repeatedly processing the full-resolution original image data.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that implement perspective-aware object move operations for digital image editing. For instance, in some embodiments, the disclosed systems determine a vanishing point associated with a digital image portraying an object. Additionally, the disclosed systems detect one or more user interactions for moving the object within the digital image. Based on moving the object with respect to the vanishing point, the disclosed systems perform a perspective-based resizing of the object within the digital image.