3D Human Model Reposing for Semantic 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 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 pixel selection and menu navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems operate on pixel level, then editing precision is maintained, but user interaction complexity and time consumption increase significantly
Solution Approach 1:
The patent transitions from two-dimensional pixel-level editing to three-dimensional semantic-level editing by introducing depth maps and 3D human models. Users can select and edit entire objects (e.g., a person) as unified 3D entities rather than individually selecting pixels, fundamentally changing the editing dimension from 2D to 3D semantic space.
Solution Approach 2:
The system segments the image into distinct semantic components (sky, ground, human, clothing) using machine learning models and depth maps. This segmentation allows users to interact with specific semantic regions independently, reducing the complexity of selecting and editing individual pixels while maintaining precise control over different image elements.
2Productivity
If conventional systems require significant user interaction for editing, then editing control is maintained, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic background generation and image synthesis without requiring users to manually specify editing parameters. After users select a semantic region (e.g., a person), the system automatically generates the background, handles pose repositioning, and synthesizes the final image, making the system serve itself for complex editing tasks.
Solution Approach 2:
The system pre-processes the image by generating depth maps, segmenting semantic regions, and creating 3D human models before actual editing occurs. These preliminary actions prepare the image data structure, enabling users to perform edits with minimal interaction while the system handles the computationally intensive processing in advance.
3Reliability
If conventional systems edit images without maintaining real-world conditions, then editing flexibility is achieved, but realism and consistency deteriorate
Solution Approach 1:
The system applies different processing qualities to different regions of the image based on their semantic meaning. For example, the sky region undergoes different background generation processing compared to the ground or human regions. This local quality approach ensures that each semantic region maintains its real-world characteristics while allowing flexible editing of specific areas.
Solution Approach 2:
The system changes multiple parameters simultaneously (depth map values, pose parameters, lighting conditions, background synthesis parameters) to maintain real-world consistency during edits. By coordinating changes across these parameters, the system ensures that edited images maintain physical realism while achieving desired editing flexibility.
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.