18055590. MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING ITERATIVE THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES simplified abstract (ADOBE INC.)
Contents
- 1 MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING ITERATIVE THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING ITERATIVE THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING ITERATIVE THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES
Organization Name
Inventor(s)
Radomir Mech of Mountain View CA (US)
Nathan Carr of San Jose CA (US)
Matheus Gadelha of San Jose CA (US)
MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING ITERATIVE THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18055590 titled 'MODIFYING TWO-DIMENSIONAL IMAGES UTILIZING ITERATIVE THREE-DIMENSIONAL MESHES OF THE TWO-DIMENSIONAL IMAGES
Simplified Explanation
The patent application describes a system for generating three-dimensional meshes representing two-dimensional images for editing purposes. The system utilizes neural networks to determine density values of pixels, sample points in the image, generate a tessellation, estimate camera parameters, and modify the mesh based on the camera parameters.
- The system uses a first neural network to determine density values of pixels in a two-dimensional image based on estimated disparity.
- Points in the image are sampled according to the density values to generate a tessellation.
- A second neural network is used to estimate camera parameters and modify the three-dimensional mesh based on the estimated camera parameters of the pixels in the two-dimensional image.
- The system can also generate a three-dimensional mesh to modify a two-dimensional image according to a displacement input, mapping the mesh to the image, modifying it in response to the input, and updating the image.
Potential Applications
This technology could be applied in various fields such as:
- Graphic design
- Virtual reality
- Augmented reality
- Animation
Problems Solved
This technology helps in:
- Enhancing image editing capabilities
- Creating realistic 3D representations from 2D images
- Improving visualization techniques
Benefits
The benefits of this technology include:
- Streamlining the editing process
- Enhancing creativity in design
- Providing more realistic visualizations
Potential Commercial Applications
Potential commercial applications of this technology could include:
- Software development for graphic design
- Tools for virtual and augmented reality content creation
- Animation software
Possible Prior Art
One possible prior art could be the use of neural networks in image processing and editing tools. Another could be the use of 3D modeling software for creating meshes from 2D images.
Unanswered Questions
How does this technology compare to existing image editing tools?
This article does not provide a direct comparison to existing image editing tools in terms of features, capabilities, or performance.
What are the limitations of this technology in practical applications?
The article does not address any potential limitations or challenges that may arise when implementing this technology in real-world scenarios.
Original Abstract Submitted
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.
