Scene-Based Object Repositioning With Consistent Shadow Generation
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to edit digital images, as they operate on a pixel level and lack the ability to intuitively handle objects as cohesive units while maintaining real-world conditions.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, segmenting objects and generating content fills, enabling intuitive and efficient object-aware modifications by treating objects as distinct units and maintaining real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems operate on pixel level, then detailed image manipulation is possible, but user interaction complexity increases significantly
Solution Approach 1:
The system segments the image into distinct object regions using machine learning models, allowing users to edit entire objects as unified entities rather than manipulating individual pixels. This segmentation enables intuitive object-aware editing while automatically handling the complex pixel-level operations in the background.
Solution Approach 2:
The patent introduces an intermediary layer between user input and pixel manipulation, where machine learning models interpret user intent and automatically generate appropriate editing operations. This intermediary translates simple user actions into complex sequences of pixel-level modifications, reducing the perceived complexity for users.
2Productivity
If machine learning models pre-process images to segment objects, then editing efficiency improves, but computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images to segment objects and generate object masks before editing operations are needed. This preparation work is done once and cached, so subsequent edits on the same image require minimal additional computation, improving efficiency while amortizing the computational cost over multiple editing operations.
3Adaptability or versatility
If objects are moved to new locations, then scene composition flexibility improves, but shadow consistency becomes difficult to maintain
Solution Approach 1:
The system dynamically adjusts shadow properties based on the object's new position and orientation in the scene. When objects are moved, the machine learning models automatically recalculate and regenerate shadows to match the new spatial relationships, maintaining visual consistency while allowing flexible scene composition.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors object positions and automatically adjusts shadow parameters accordingly. This closed-loop approach ensures that shadows remain consistent with the current scene configuration, providing reliable visual feedback that reinforces the flexibility of object manipulation.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides a graphical user interface experience to move objects and generate new shadows within a digital image scene. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems receive a selection to position an object in a first location within the scene. Further, the disclosed systems composite an image by placing the object at the first location within the scene of the digital image. Moreover, the disclosed systems generate a modified digital image having a shadow of the object where the shadow is consistent with the scene and provides the modified digital image to the client device.