Segmented 3D Meshes for Accurate 2D Image Editing
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Solution Overview
Problem
Conventional image editing systems require significant time, user interactions, and resources to achieve accurate and realistic modifications in two-dimensional images, often lacking efficiency and accuracy due to the need for manual pixel editing and multiple tools.
Innovation Solution
A depth displacement system that generates adaptive three-dimensional meshes from two-dimensional images, utilizing neural networks to estimate depth and camera parameters, allowing for efficient and intuitive modifications by displacing portions of the three-dimensional mesh, which are then mapped back to the two-dimensional image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual pixel editing and multiple image editing tools are used, then accurate and realistic modifications can be achieved, but significant time and user interactions are required
Solution Approach 1:
The patent converts two-dimensional image editing into three-dimensional mesh manipulation by generating a depth map and corresponding 3D mesh from the 2D image. Users can select and modify objects in 3D space, allowing for intuitive transformations (move, resize, rotate) that are then projected back to 2D, achieving accurate edits without manual pixel manipulation.
Solution Approach 2:
The patent introduces a three-dimensional mesh as an intermediary representation between the original 2D image and the final edited result. This mesh serves as a mediator that enables sophisticated object manipulation while maintaining the simplicity of 2D image output, resolving the contradiction between editing power and user effort.
2Adaptability or versatility
If conventional image editing tools are used, then modifications can be made, but significant computational resources and complexity are required
Solution Approach 1:
The patent segments the image into distinct objects by generating a depth map that separates foreground objects from the background. This segmentation is achieved through neural network-based depth estimation, creating discrete 3D mesh objects that can be independently manipulated, reducing the complexity of selecting and editing specific regions.
Solution Approach 2:
The patent replaces complex manual editing mechanics with automated neural network-based depth map generation and 3D mesh construction. The system automatically performs tasks that would traditionally require multiple manual operations (selection, masking, transformation), thereby reducing system complexity while maintaining versatility.
3Productivity
If adaptive three-dimensional meshes are generated and used for editing, then fast and accurate editing with reduced computational expense is achieved, but the system requires neural networks for depth estimation
Solution Approach 1:
The patent performs preliminary depth map generation and 3D mesh construction automatically using neural networks before the actual editing operation. This pre-processing step creates a structured 3D representation that enables rapid subsequent editing operations, achieving high productivity while the initial complexity is handled automatically.
Solution Approach 2:
The system uses self-service mechanisms where the neural network automatically generates the depth map and 3D mesh without user intervention. The complexity of depth estimation and mesh generation is handled autonomously by the system, allowing users to benefit from fast editing without directly managing the complex preprocessing steps.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating three-dimensional meshes representing two-dimensional images for editing the two-dimensional images. The disclosed system utilizes a first neural network to determine density values of pixels of a two-dimensional image based on estimated disparity. The disclosed system samples points in the two-dimensional image according to the density values and generates a tessellation based on the sampled points. The disclosed system utilizes a second neural network to estimate camera parameters and modify the three-dimensional mesh based on the estimated camera parameters of the pixels of the two-dimensional image. In one or more additional embodiments, the disclosed system generates a three-dimensional mesh to modify a two-dimensional image according to a displacement input. Specifically, the disclosed system maps the three-dimensional mesh to the two-dimensional image, modifies the three-dimensional mesh in response to a displacement input, and updates the two-dimensional image.