3D Shadow Maps for Scene-Based 2D Image Editing
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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 user interactions on a semantic level by treating objects as distinct units and maintaining real-world conditions, reducing the need for manual editing steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems operate on a pixel level, then detailed image manipulation is possible, but user interaction complexity and specialized knowledge requirements increase significantly
Solution Approach 1:
The patent segments the image into multiple depth layers representing different depth planes. Each layer contains pixels that belong to objects at similar depths, allowing users to edit entire depth layers or objects within them as unified units rather than individual pixels. This segmentation enables intuitive editing operations while maintaining detailed manipulation capabilities.
Solution Approach 2:
The patent introduces a depth dimension to traditional 2D image editing by organizing pixels into 3D spatial coordinates (x, y, depth). This additional dimension allows users to interact with images based on real-world spatial relationships, selecting and editing objects based on their depth position rather than pixel coordinates, thereby simplifying the editing process while preserving detail.
2Productivity
If conventional image editing systems require manual editing steps for each operation, then precise control is achieved, but editing efficiency and productivity decrease
Solution Approach 1:
The patent performs preliminary actions by automatically analyzing the input image, determining depth information for each pixel, and organizing pixels into depth layers before the user begins editing. This pre-processing enables users to immediately interact with organized depth layers without manual setup, significantly reducing the time required for editing operations while maintaining precise control over edited elements.
3Reliability
If conventional image editing systems treat all pixels uniformly, then simple processing is possible, but the ability to maintain real-world conditions and semantic understanding is lost
Solution Approach 1:
The patent applies local quality by assigning different depth values and properties to different regions of the image based on their spatial characteristics. Each pixel is associated with specific depth information and belongs to particular depth layers, enabling the system to maintain real-world spatial relationships and semantic understanding for each local region while processing the entire image coherently.
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.