View source for 17972032. INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS simplified abstract (Intel Corporation)

=INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS=


==Organization Name==

[[:Category:Intel Corporation|Intel Corporation]]


[[Category:Intel Corporation]]

==Inventor(s)==

[[:Category:Shengze Wang of Santa Clara CA (US)|Shengze Wang of Santa Clara CA (US)]][[Category:Shengze Wang of Santa Clara CA (US)]]

[[:Category:Alexey Supikov of Santa Clara CA (US)|Alexey Supikov of Santa Clara CA (US)]][[Category:Alexey Supikov of Santa Clara CA (US)]]

[[:Category:Joshua Ratcliff of San Jose CA (US)|Joshua Ratcliff of San Jose CA (US)]][[Category:Joshua Ratcliff of San Jose CA (US)]]

[[:Category:Ronald Azuma of San Jose CA (US)|Ronald Azuma of San Jose CA (US)]][[Category: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 17972032 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 weight transferal. This innovation involves a system interconnect and a graphics processor cluster with configurable circuitry for this purpose.

* System interconnect and graphics processor cluster work together to generate per-frame neural representations of a multi-view video.
* Circuitry in the graphics processor cluster is configurable for incremental training and transferal of weights.

== Potential Applications ==
The technology could be applied in virtual reality systems, augmented reality devices, and 3D content creation tools.

== Problems Solved ==
This innovation addresses the challenge of efficiently processing multi-view video data and generating neural representations in real-time.

== Benefits ==
The benefits of this technology include improved performance in processing multi-view video, enhanced realism in virtual and augmented reality applications, and more efficient 3D content creation.

== Potential Commercial Applications ==
A potential commercial application of this technology could be in the development of advanced graphics processors for gaming consoles, VR headsets, and professional video editing systems.

== Possible Prior Art ==
One possible prior art could be the use of graphics processors for video processing and neural network applications, but the specific approach of incremental training and weight transferal for generating per-frame neural representations may be novel.

=== Unanswered Questions ===

=== How does this technology compare to existing methods for generating neural representations of multi-view video? ===
This article does not provide a direct comparison with existing methods or technologies in the field.

=== What are the limitations or constraints of the configurable circuitry in the graphics processor cluster? ===
The article does not address any potential limitations or constraints of the configurable circuitry in the graphics processor cluster.


==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.



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