Adaptive 3D Mesh Generation for Realistic 2D Image Editing
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Solution Overview
Problem
Conventional image editing systems are time-consuming, computationally expensive, and inaccurate when performing complex modifications on two-dimensional images, lacking efficiency and realism due to their operation within two-dimensional coordinate systems.
Innovation Solution
A depth displacement system generates adaptive three-dimensional meshes from two-dimensional images using neural networks to represent depth displacement, allowing for efficient and accurate modifications by leveraging three-dimensional space, including geometry displacement, texture changes, and shading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image editing tools are used to modify two-dimensional images, then editing functionality is provided, but the process is time-consuming and computationally expensive
Solution Approach 1:
The patent transforms two-dimensional image editing into three-dimensional mesh manipulation. By generating a three-dimensional mesh representation of the image and performing edits in this additional dimension, the system achieves faster and more efficient modifications while maintaining the original two-dimensional image quality.
2Manufacturing precision
If conventional image editing tools are used, then basic editing is possible, but complex modifications lack accuracy and realism
Solution Approach 1:
By operating in three-dimensional space rather than two-dimensional coordinate systems, the patent enables more accurate and realistic complex modifications. The third dimension provides additional degrees of freedom for manipulation, allowing for more precise control over image elements and their relationships.
3Adaptability or versatility
If image editing applications are adapted for devices with limited processing resources, then device compatibility is improved, but editing capability is reduced
Solution Approach 1:
The patent replaces computationally intensive mechanical image processing operations with neural network-based operations. The neural networks perform complex tasks such as mesh generation, depth estimation, and image modification more efficiently, enabling high-capability editing on devices with limited processing resources.
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.