Intel corporation (20240135483). INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS simplified abstract
Contents
- 1 INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS - 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
INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS
Organization Name
Inventor(s)
Shengze Wang of Santa Clara CA (US)
Alexey Supikov of Santa Clara CA (US)
Joshua Ratcliff of San Jose CA (US)
Ronald Azuma of San Jose CA (US)
INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135483 titled 'INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS
Simplified Explanation
The graphics processor described in the patent application is capable of generating per-frame neural representations of a multi-view video through incremental training and transferal of weights. This innovation involves a system interconnect and a graphics processor cluster that work together to achieve this functionality.
- The graphics processor comprises a system interconnect and a graphics processor cluster.
- The graphics processor cluster includes circuitry configurable to generate per-frame neural representations of a multi-view video via incremental training and transferal of weights.
Potential Applications
This technology could be applied in:
- Virtual reality systems
- Video streaming platforms
- Gaming consoles
Problems Solved
This technology addresses the following issues:
- Efficient generation of neural representations for multi-view videos
- Real-time processing of complex video data
Benefits
The benefits of this technology include:
- Enhanced video quality
- Reduced processing time
- Improved user experience
Potential Commercial Applications
With its capabilities, this technology could be utilized in:
- Entertainment industry
- Security and surveillance systems
- Medical imaging technologies
Possible Prior Art
One possible prior art for this technology could be:
- Graphics processors with neural network capabilities
Unanswered Questions
How does this technology impact energy consumption in graphics processing units?
This article does not delve into the energy efficiency aspect of the graphics processor described. Further research is needed to understand the impact on energy consumption.
What are the potential limitations of incremental training and weight transferal in generating neural representations?
The article does not address any potential limitations or challenges that may arise from the incremental training and weight transferal process. Further investigation is required to explore any drawbacks or constraints of this approach.
Original Abstract Submitted
described herein is a graphics processor comprising a system interconnect and a graphics processor cluster coupled with the system interconnect. the graphics processor cluster includes circuitry configurable to generate per-frame neural representations of a multi-view video via incremental training and transferal of weights.
