Google llc (20240202878). Image Transformation Using Interpretable Transformation Parameters simplified abstract
Contents
Image Transformation Using Interpretable Transformation Parameters
Organization Name
Inventor(s)
Diego Martin Arroyo of Zürich (CH)
Alessio Tonioni of Zürich (CH)
Image Transformation Using Interpretable Transformation Parameters - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202878 titled 'Image Transformation Using Interpretable Transformation Parameters
Simplified Explanation: The patent application describes a computer-implemented method for image-to-image translation using machine-learned generator models.
Key Features and Innovation:
- Obtaining machine-learned generator models
- Configuring models to receive input image and user-specified conditioning vector
- Performing transformations on input image based on conditioning vector to generate output image with desired characteristics
Potential Applications: This technology can be used in various fields such as computer vision, image editing, and digital art creation.
Problems Solved: This technology addresses the challenge of translating images from one domain to another while preserving desired characteristics.
Benefits:
- Efficient image-to-image translation
- Customizable output based on user-specified conditioning vector
- High-quality output images with desired characteristics
Commercial Applications: The technology can be applied in industries such as graphic design, advertising, and entertainment for creating visually appealing content efficiently.
Prior Art: Readers can explore prior art related to image-to-image translation, machine learning, and computer vision techniques for further research.
Frequently Updated Research: Stay updated on advancements in machine learning models for image translation and related technologies to enhance understanding and application of this innovation.
Questions about Image-to-Image Translation: 1. What are the key challenges in image-to-image translation? 2. How does the use of conditioning vectors improve the output of image translation models?
Original Abstract Submitted
. a computer-implemented method to perform image-to-image translation. the method can include obtaining one or more machine-learned generator models. the one or more machine-learned generator models can be configured to receive an input image and a user-specified conditioning vector that parameterizes one or more desired values for one or more defined characteristics of an output image. the one or more machine-learned generator models can be configured to perform, based at least in part on the user-specified conditioning vector, one or more transformations on the input image to generate the output image with the one or more desired values for the one or more defined characteristics. the method can include receiving the input image and the user-specified conditioning vector. the method can include generating, using the machine-learned generator model, an output image having the one or more desired values for the one or more characteristics.
