18453248. TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS simplified abstract (NVIDIA Corporation)
Contents
- 1 TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS
Organization Name
Inventor(s)
Xueting Li of Santa Clara CA (US)
Stanley Birchfield of Sammamish WA (US)
Shalini De Mello of San Francisco CA (US)
Sifei Liu of Santa Clara CA (US)
Jiaming Song of San Carlos CA (US)
Yufei Ye of Pittsburgh PA (US)
TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18453248 titled 'TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS
Simplified Explanation
The abstract describes a patent application for techniques to generate an image using machine learning models for denoising operations.
- The techniques involve using a first machine learning model to generate a mask indicating the spatial arrangement of a second object interacting with a first object in an input image.
- A second machine learning model is then used along with the input image and the mask to generate an image of the second object interacting with the first object.
Potential Applications
The technology could be applied in various fields such as computer vision, image processing, and augmented reality for enhancing image quality and generating realistic visual effects.
Problems Solved
1. Improved image generation by utilizing machine learning models for denoising operations. 2. Enhancing the spatial arrangement of objects in images for more realistic visual effects.
Benefits
1. Higher quality image generation with reduced noise and improved object interactions. 2. Automation of image enhancement processes for efficiency and accuracy.
Potential Commercial Applications
"Machine Learning Image Generation Techniques for Enhanced Visual Effects" could find applications in industries such as entertainment (special effects in movies), gaming (realistic graphics), and advertising (product visualization).
Possible Prior Art
There may be prior art related to image denoising techniques using machine learning models, but specific examples are not provided in the abstract.
Unanswered Questions
How does the accuracy of the generated image compare to traditional image processing techniques?
The abstract does not mention any comparison with traditional methods for image generation and denoising.
Are there any limitations to the size or complexity of images that can be processed using these techniques?
It is not clear from the abstract whether there are any limitations in terms of image size or complexity that could affect the effectiveness of the proposed techniques.
Original Abstract Submitted
Techniques are disclosed herein for generating an image. The techniques include performing one or more first denoising operations based on a first machine learning model and an input image that includes a first object to generate a mask that indicates a spatial arrangement associated with a second object interacting with the first object, and performing one or more second denoising operations based on a second machine learning model, the input image, and the mask to generate an image of the second object interacting with the first object.
