Depth-Aware Object Move in Digital Image Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring users to interact with individual pixels and perform multiple steps to edit digital images, which demands deep knowledge of the system and user interface, leading to a cumbersome 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, allowing users to interact with semantic areas like objects directly, reducing the need for pixel-level editing and simplifying the editing process by pre-processing images to anticipate user interactions and maintain real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image editing systems require users to interact with individual pixels, then editing precision can be achieved, but the ease of operation deteriorates significantly
Solution Approach 1:
The patent segments the image into distinct object regions using machine learning models, allowing users to edit entire objects by selecting them as unified entities rather than manipulating individual pixels. This segmentation maintains editing precision while dramatically improving ease of operation through intuitive object-based selection and modification.
2Ease of operation
If conventional systems require multiple editing steps, then editing control is improved, but productivity deteriorates due to the cumbersome process
Solution Approach 1:
The system performs preliminary actions by automatically segmenting objects and generating editable object representations before the user initiates editing. This pre-processing enables users to directly manipulate ready-to-edit objects, eliminating multiple preparatory steps and significantly improving productivity while maintaining editing control.
3Device complexity
If the system processes images as two-dimensional pixel arrays, then data structure simplicity is maintained, but adaptability to real-world editing needs deteriorates
Solution Approach 1:
The patent introduces a semantic dimension to traditional two-dimensional image processing by overlaying object segmentation masks and depth information. This multi-dimensional approach enables the system to understand and edit images according to real-world three-dimensional object structures, significantly improving adaptability while building upon the existing two-dimensional data structure.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that implement depth-aware object move operations for digital image editing. For instance, in some embodiments, the disclosed systems determine a first object depth for a first object portrayed within a digital image and a second object depth for a second object portrayed within the digital image. Additionally, the disclosed systems move the first object to create an overlap area between the first object and the second object within the digital image. Based on the first object depth and the second object depth, the disclosed systems modify the digital image to occlude the first object or the second object within the overlap area.