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For the Inventor, By the Inventor

San Jose, CA, United States of America

Jinwei Gu

USPTO Granted Patents = 14 

Average Co-Inventor Count = 4.4

ph-index = 4

Forward Citations = 28(Granted Patents)


Company Filing History:

goldMedal14 out of 5,406 
 
Nvidia Corporation
 patents

Years Active: 2019-2025

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14 patents (USPTO):
1. 12462453 - Context-aware synthesis and placement of object instances (2025-11-04),
2. 11328173 - Switchable propagation neural network (2022-05-10),
3. 11328169 - Switchable propagation neural network (2022-05-10),
4. 11295514 - Inverse rendering of a scene from a single image (2022-04-05),
5. 11270161 - Deep-learning method for separating reflection and transmission images visible at a semi-reflective surface in a computer image of a real-world scene (2022-03-08),
6. 11037051 - 3D plane detection and reconstruction using a monocular image (2021-06-15),
7. 10984545 - Estimating depth for a video stream captured with a monocular rgb camera (2021-04-20),
8. 10964061 - Learning-based camera pose estimation from images of an environment (2021-03-30),
9. 10922793 - Guided hallucination for missing image content using a neural network (2021-02-16),
10. 10762620 - Deep-learning method for separating reflection and transmission images visible at a semi-reflective surface in a computer image of a real-world scene (2020-09-01),
11. 10762425 - Learning affinity via a spatial propagation neural network (2020-09-01),
12. 10692244 - Learning based camera pose estimation from images of an environment (2020-06-23),
13. 10593020 - Unsupervised learning approach for video deblurring (2020-03-17),
14. 10373332 - Systems and methods for dynamic facial analysis using a recurrent neural network (2019-08-06),

Title: **Innovations of Jinwei Gu: A Visionary in Video Image Processing**

Introduction

Jinwei Gu, an innovative inventor based in San Jose, California, has made significant contributions to the field of video image processing. With a remarkable portfolio of 13 patents, Gu is recognized for his groundbreaking work in developing advanced neural network systems aimed at enhancing visual data processing.

Latest Patents

One of his latest innovations is the Switchable Propagation Neural Network (TPN) system. This temporal propagation network is designed to intelligently learn the affinity matrix, which defines the similarities between different points in space, specifically for video image processing tasks. The TPN system comprises a guidance neural network model and a temporal propagation module, trained for specific computer vision challenges. The system efficiently propagates visual properties from a key-frame represented by dense color data to a subsequent frame depicted by coarse grey-scale data. By generating a global transformation matrix, the guidance neural network effectively allows for the colorization of grey-scale video frames based on a single manually colorized key-frame. This innovative approach showcases Gu's ability to enhance visual data interpretation and usability in various applications.

Career Highlights

Jinwei Gu is currently employed at Nvidia Corporation, where he continues to push the boundaries of technology in video processing. His dedication to advancing machine learning and computer vision has not only established him as a leader in his field but also contributed to the broader landscape of innovation in artificial intelligence.

Collaborations

During his career, Gu has collaborated with notable colleagues such as Jan Kautz and Kihwan Kim. Together, they have fostered a creative environment that encourages the exchange of ideas and solutions, culminating in several impactful technological advancements.

Conclusion

In summary, Jinwei Gu exemplifies the spirit of innovation through his contributions to video image processing and neural network systems. With a solid foundation of patents and collaborative efforts with esteemed colleagues, Gu continues to inspire and lead in the ever-evolving landscape of technology. His work not only enhances current methodologies but also paves the way for future advancements in the field.

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