3D Scene-Based Editing for Intuitive 2D Focal Control
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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 and fail to maintain real-world conditions during edits.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, allowing intuitive, semantic-level editing by treating objects as distinct units and maintaining real-world conditions, reducing the need for user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems operate on pixel level, then detailed editing control is achieved, but user interaction complexity and specialized knowledge requirements increase significantly
Solution Approach 1:
The patent segments the image into distinct object regions using machine learning models, allowing users to edit semantic objects rather than individual pixels. This segmentation enables intuitive selection and manipulation of image components without requiring users to understand pixel-level operations, thereby improving ease of operation while reducing interaction complexity
Solution Approach 2:
The patent introduces machine learning models as intermediaries that automatically perform complex image analysis and editing tasks. These models serve as mediators between user intent and image modification, handling the complexity of pixel-level operations internally while presenting simple, intuitive controls to users, thus resolving the contradiction between operational ease and system complexity
2Productivity
If conventional image editing systems require multiple tool interactions, then precise editing control is achieved, but editing efficiency and productivity decrease
Solution Approach 1:
The patent performs preliminary actions by pre-processing the image to identify and segment objects before user editing begins. The machine learning models automatically analyze the image structure, generate object masks, and prepare editable regions in advance, so that users can directly interact with identified objects without needing to perform multiple sequential editing operations, thereby improving productivity while reducing interaction steps
Solution Approach 2:
The patent enables the system to perform self-service by automatically maintaining real-world conditions and performing corrective operations without user intervention. The machine learning models autonomously handle tasks such as maintaining lighting consistency, adjusting shadows, and preserving object relationships, eliminating the need for users to manually perform these complex operations and thus improving editing efficiency
3Reliability
If conventional image editing systems do not maintain real-world conditions, then editing flexibility is reduced, but processing speed increases
Solution Approach 1:
The patent performs preliminary analysis to understand real-world conditions in the image, such as lighting directions, object depths, and spatial relationships, before editing operations begin. This pre-processing enables the system to automatically maintain these conditions during editing without requiring multiple corrective passes, thereby ensuring reliability while minimizing additional processing time
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning models continuously monitor and adjust edited regions to maintain real-world conditions. The system provides automatic feedback on lighting consistency, shadow alignment, and object relationships, making real-time corrections to ensure physical realism is preserved throughout the editing process, thus achieving both reliability and acceptable processing speed
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.