View source for GM CRUISE HOLDINGS LLC (20240282117). APPROXIMATELY-PAIRED SIMULATION-TO-REAL IMAGE TRANSLATION simplified abstract

=APPROXIMATELY-PAIRED SIMULATION-TO-REAL IMAGE TRANSLATION=


==Organization Name==

[[:Category:GM CRUISE HOLDINGS LLC|GM CRUISE HOLDINGS LLC]]


[[Category:GM CRUISE HOLDINGS LLC]]

==Inventor(s)==

[[:Category:Ashish Shrivastava of San Jose CA (US)|Ashish Shrivastava of San Jose CA (US)]][[Category:Ashish Shrivastava of San Jose CA (US)]]

[[:Category:Charles Yingjia Zhang of Ottawa (CA)|Charles Yingjia Zhang of Ottawa (CA)]][[Category:Charles Yingjia Zhang of Ottawa (CA)]]

==APPROXIMATELY-PAIRED SIMULATION-TO-REAL IMAGE TRANSLATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240282117 titled 'APPROXIMATELY-PAIRED SIMULATION-TO-REAL IMAGE TRANSLATION

==Simplified Explanation==
The patent application describes a method for translating simulated images to real images using a generative adversarial network (GAN) model.

* Receiving an image pair with a real image and a simulated image.
* Determining style differences between the simulated and real images.
* Training the GAN to generate a post-processed simulated image.

==Key Features and Innovation==
* Method for translating simulated images to real images.
* Utilizes a style encoder to determine style differences.
* Trains a GAN to generate post-processed images.

==Potential Applications==
* Image editing and enhancement.
* Virtual reality and augmented reality applications.
* Gaming graphics development.

==Problems Solved==
* Improves the quality and realism of simulated images.
* Streamlines the process of translating simulated images to real images.

==Benefits==
* Enhances visual quality in various applications.
* Increases efficiency in image translation processes.

==Commercial Applications==
* "Facilitating approximately-paired simulation-to-real image translation" technology can be used in industries such as entertainment, design, and virtual reality development to enhance visual content and streamline image translation processes.

==Prior Art==
There may be prior art related to image translation methods using GAN models and style encoders that could provide additional insights into this technology.

==Frequently Updated Research==
Researchers are constantly exploring new techniques and improvements in image translation using GAN models, which could impact the development of this technology.

==Questions about Simulation-to-Real Image Translation==
===How does this technology improve the quality of simulated images?===
This technology improves the quality of simulated images by using a GAN model to generate post-processed images that closely resemble real images, enhancing visual realism and detail.

===What are the potential applications of this image translation method beyond entertainment and gaming?===
This image translation method can also be applied in fields such as medical imaging, satellite imagery analysis, and industrial design to enhance image quality and facilitate data analysis.


==Original Abstract Submitted==

disclosed are embodiments for facilitating approximately-paired simulation-to-real image translation. in some aspects, a method includes receiving, by a processing device performing training on a model, an approximately-paired image pair comprising a real image and a simulated image, wherein the simulated image is generated from the real image using contextual data of the real image; determining, by the processing device using a style encoder of the model, a style difference between a first style vector of the simulated image and a second style vector of the real image, wherein the first style vector and the second style vector encode style features of the simulated image and the real image using a style encoder; and inputting the style difference and the simulated image to a generative adversarial network (gan) of the model to train the gan to generate a post-processed simulated image.



[[Category:G06V20/58]]
[[Category:B60W60/00]]
[[Category:CPC_G06V20/58]]