Generative Inpainting for Texture-Preserving Shadow Removal
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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, failing to maintain real-world conditions during editing.
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, object-aware modifications that maintain real-world conditions without additional user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional pixel-level image editing is used, then precise control over image details is achieved, but user interaction complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the image into distinct object regions using machine learning models, allowing users to edit entire objects with single operations rather than manually editing individual pixels. This segmentation enables object-aware editing where users can select and modify semantic regions (e.g., sky, buildings, vehicles) directly, dramatically reducing the number of interactions needed while maintaining precise control over image content.
Solution Approach 2:
The system performs preliminary processing by pre-segmenting the image into editable object regions and pre-processing shadow areas before user interaction. This preliminary segmentation and shadow identification allows the system to anticipate user editing needs and prepare modification masks in advance, reducing the time required for actual editing operations.
2Adaptability or versatility
If object-based editing is implemented, then editing flexibility improves, but system complexity increases due to need for object segmentation and identification
Solution Approach 1:
The patent employs a multi-functional machine learning system that performs object segmentation, shadow detection, and edit mask generation using integrated neural network models. These models serve multiple purposes: identifying objects for editing, detecting shadow regions, and generating appropriate modification masks, thereby managing system complexity through unified multi-functional processing rather than separate specialized systems.
Solution Approach 2:
The system automatically performs object identification, shadow detection, and edit mask generation without requiring user input for these complex processing tasks. The machine learning models self-service by autonomously segmenting objects, identifying shadow regions, and creating modification masks based on user selections, reducing the complexity of user interactions while maintaining flexible object-based editing capabilities.
3Object-generated harmful factors
If shadows are removed using conventional methods, then shadow elimination is achieved, but texture information at shadowed locations is lost
Solution Approach 1:
The patent uses generative inpainting models to copy and reconstruct texture information from surrounding non-shadowed regions of the image. When removing shadows, the system analyzes the texture patterns in adjacent areas and generates realistic texture content to replace the shadowed regions, effectively copying visual information from one part of the image to another while maintaining overall image consistency and realism.
Solution Approach 2:
The system replaces conventional mechanical shadow removal methods (such as simple pixel manipulation or cloning) with advanced generative inpainting neural networks. These AI-based models substitute traditional processing mechanics with learned patterns from training data, enabling sophisticated texture reconstruction that preserves realistic surface details while eliminating shadows, achieving both effective shadow removal and texture preservation simultaneously.
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 to remove a shadow for an object. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access a shadow mask of the shadow in a first location. Further, the disclosed systems generate the modified digital image without the shadow by generating a fill for the first location that preserves a visible location of the first location. Moreover, the disclosed systems generate the digital image without the shadow for the object by combining the fill with the digital image.