Nvidia corporation (20240161468). TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS simplified abstract
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 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 20240161468 titled 'TECHNIQUES FOR GENERATING IMAGES OF OBJECT INTERACTIONS
Simplified Explanation
The abstract describes techniques for generating an image using machine learning models to denoise and enhance the image. The process involves creating a mask to indicate the spatial arrangement of objects in the image and then generating the final image based on this mask.
- First denoising operations based on a machine learning model and input image
- Mask generation to indicate spatial arrangement of objects
- Second denoising operations using a different machine learning model and the mask
- Image generation of objects interacting based on the mask
Potential Applications
This technology could be applied in various fields such as medical imaging, surveillance, and entertainment industries for enhancing and clarifying images of objects interacting with each other.
Problems Solved
1. Noise reduction in images 2. Enhancing spatial arrangement of objects in images
Benefits
1. Improved image quality 2. Enhanced visualization of object interactions
Potential Commercial Applications
"Enhancing Image Quality with Machine Learning Models" could be used in industries such as healthcare for improving medical imaging, in security for clearer surveillance footage, and in entertainment for creating realistic visual effects.
Possible Prior Art
One possible prior art could be traditional image denoising techniques that do not involve machine learning models.
What are the limitations of this technology in real-world applications?
There may be limitations in processing time and computational resources required for implementing these techniques in real-time applications.
How does this technology compare to existing image enhancement methods?
This technology offers the advantage of utilizing machine learning models to enhance images, which may result in better noise reduction and object interaction visualization compared to traditional methods.
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.
