Nvidia corporation (20240338871). CONTEXT-AWARE SYNTHESIS AND PLACEMENT OF OBJECT INSTANCES simplified abstract
Contents
CONTEXT-AWARE SYNTHESIS AND PLACEMENT OF OBJECT INSTANCES
Organization Name
Inventor(s)
Donghoom Lee of Sunnyvale CA (US)
Sifei Liu of Santa Clara CA (US)
Ming-Yu Liu of San Jose CA (US)
Jan Kautz of Lexington MA (US)
CONTEXT-AWARE SYNTHESIS AND PLACEMENT OF OBJECT INSTANCES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338871 titled 'CONTEXT-AWARE SYNTHESIS AND PLACEMENT OF OBJECT INSTANCES
The abstract of this patent application describes a method that involves using generator models to manipulate images and insert objects into them.
- Applying a first generator model to a semantic representation of an image to generate an affine transformation representing a bounding box of a region within the image.
- Applying a second generator model to the affine transformation and the semantic representation to generate the shape of an object.
- Inserting the object into the image based on the bounding box and the shape.
Potential Applications: - Image editing software - Augmented reality applications - Object recognition systems
Problems Solved: - Efficiently inserting objects into images - Enhancing the realism of augmented reality experiences
Benefits: - Improved image manipulation capabilities - Enhanced visual effects in augmented reality
Commercial Applications: - Development of advanced image editing tools - Integration into augmented reality platforms for enhanced user experiences
Questions about the technology: 1. How does this method improve upon existing image manipulation techniques? 2. What are the potential limitations of using generator models for object insertion in images?
Frequently Updated Research: - Stay updated on advancements in generator model technology for image manipulation and object insertion.
Original Abstract Submitted
one embodiment of a method includes applying a first generator model to a semantic representation of an image to generate an affine transformation, where the affine transformation represents a bounding box associated with at least one region within the image. the method further includes applying a second generator model to the affine transformation and the semantic representation to generate a shape of an object. the method further includes inserting the object into the image based on the bounding box and the shape.
