Object-Shadow Pair Detection for Scene-Based Image Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to perform edits on a pixel level and failing to maintain real-world conditions during modifications.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, identifying objects and their relationships, and generating content fills, allowing for intuitive, object-level interactions and efficient editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems perform pixel-level edits, then editing precision is maintained, but user interaction complexity and time consumption increase significantly
Solution Approach 1:
The system segments the image into multiple objects using object detection and segmentation models. Each object is identified with bounding boxes and segmentation masks, allowing users to interact with objects as cohesive units rather than individual pixels. This segmentation enables selective editing of specific objects while automatically preserving the relationships with other objects in the scene.
Solution Approach 2:
The system introduces an intermediary processing layer between user input and pixel-level editing. This layer includes scene graph generation, object relationship analysis, and automatic edit propagation mechanisms. The intermediary layer translates high-level object-level user intentions into coordinated pixel-level modifications across multiple objects, reducing direct user interaction while maintaining editing precision.
2Productivity
If conventional systems require significant user interaction for edits, then editing control is precise, but productivity and efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing the input image to generate object detections, segmentation masks, and scene graphs before user interaction. This preliminary analysis establishes object boundaries, relationships, and contextual understanding in advance, enabling efficient real-time editing operations without requiring users to manually define these parameters during the editing process.
Solution Approach 2:
The system implements feedback mechanisms where edit operations on one object automatically trigger updates to related objects based on the scene graph relationships. When an object is moved, scaled, or modified, the system provides feedback by automatically adjusting associated objects (e.g., shadows, reflections, occluded objects) to maintain scene consistency, reducing the need for manual adjustments and improving productivity.
3Adaptability or versatility
If conventional image editing modifies objects independently, then editing flexibility is achieved, but consistency with real-world conditions is lost
Solution Approach 1:
The system merges object-level editing operations with scene-level consistency constraints. By combining object detection, segmentation, and relationship analysis into a unified scene graph, the system enables flexible object-level edits while automatically enforcing real-world physical relationships. This merging allows users to edit individual objects freely while the system ensures that shadows, reflections, and spatial relationships remain consistent with the overall scene.
4Reliability
If the system processes objects individually, then processing speed is maintained, but ability to maintain real-world relationships deteriorates
Solution Approach 1:
The system implements a universal scene graph data structure and processing framework that handles multiple object relationships simultaneously. This multi-functional approach allows the same processing pipeline to manage diverse relationships (spatial, occlusion, shadow, reflection) across all objects in the scene, maintaining high object relationship accuracy without proportionally increasing processing complexity through specialized handlers for each relationship type.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems receive a digital image from a client device. The disclosed systems detect, utilizing a shadow detection neural network, an object portrayed in the digital image. The disclosed systems detect, utilizing the shadow detection neural network, a shadow portrayed in the digital image. The disclosed systems generate, utilizing the shadow detection neural network, an object-shadow pair prediction that associates the shadow with the object.