AI Multi-Layer Scene Completion for Object-Level Image Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to edit digital images due to their pixel-based approach, which necessitates deep knowledge of the system interface and multiple steps for each edit.
Innovation Solution
A scene-based image editing system that utilizes machine learning to pre-process digital images, identifying objects and their relationships, generating object masks and content fills, and enabling intuitive, object-level interactions to maintain real-world conditions during editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pixel-based approach is used for image editing, then precise control over image details is achieved, but user interaction complexity and required knowledge increase significantly
Solution Approach 1:
The patent segments the image into distinct object masks using machine learning models, allowing users to interact with entire objects rather than individual pixels. This segmentation enables precise object-level control while simplifying user interaction, as users can select and edit complete semantic objects with simple gestures or clicks instead of manually selecting pixels.
Solution Approach 2:
The patent introduces an intermediary layer of object masks and semantic understanding between the user and the pixel data. The machine learning model acts as a mediator that translates user intent at the object level into precise pixel-level modifications, thereby maintaining editing precision while greatly easing user interaction.
2Ease of operation
If multiple editing steps are required for each edit operation, then comprehensive control over editing process is achieved, but productivity and efficiency decrease
Solution Approach 1:
The patent performs preliminary actions by pre-processing the image to generate object masks, segment the scene into semantic areas, and pre-compute potential edit operations using machine learning models. This preliminary processing enables the system to anticipate user intentions and prepare multiple editing options in advance, allowing users to execute edits with minimal interactions while maintaining comprehensive control.
Solution Approach 2:
The patent creates a universal editing framework where a single user interaction can trigger multiple coordinated editing operations across different objects and layers. The system provides multi-functionality by enabling various edit types (move, resize, replace, delete) to be performed through a unified object-based interface, thereby improving productivity without sacrificing control.
3Device complexity
If the system treats the digital image as two-dimensional data, then simplicity in data structure is maintained, but understanding and editing of real-world conditions becomes difficult
Solution Approach 1:
The patent adds semantic and spatial dimensions to the two-dimensional image data by generating object masks, depth maps, and scene graphs that represent three-dimensional real-world conditions. The machine learning models infer spatial relationships, object hierarchies, and physical properties from the 2D image, enabling the system to understand and edit scenes as if they were three-dimensional while maintaining the underlying 2D data structure.
4Reliability
If significant user interaction is required for image editing, then precise user intent is captured, but time consumption and operational burden increase
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously monitors user interactions and adjusts its predictions of user intent accordingly. The system provides real-time feedback by suggesting edit operations, showing predicted outcomes, and refining object selections based on user corrections or confirmations. This feedback loop ensures accurate capture of user intent while minimizing the number of interactions required.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.