Intel corporation (20240233062). INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS simplified abstract
Contents
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 20240233062 titled 'INCREMENTAL NEURAL REPRESENTATION FOR FAST GENERATION OF DYNAMIC FREE-VIEWPOINT VIDEOS
Simplified Explanation: The patent application describes a graphics processor that can generate neural representations of multi-view videos through incremental training and weight transfer.
- The graphics processor includes a system interconnect and a graphics processor cluster.
- The cluster has circuitry that can be configured to create per-frame neural representations of multi-view videos.
- This is achieved through incremental training and transferal of weights.
Key Features and Innovation:
- Graphics processor with system interconnect and cluster.
- Circuitry configurable for generating neural representations of multi-view videos.
- Incremental training and weight transfer for creating per-frame representations.
Potential Applications: This technology could be used in virtual reality systems, video editing software, and 3D modeling applications.
Problems Solved: The technology addresses the need for efficient generation of neural representations for multi-view videos.
Benefits:
- Improved performance in processing multi-view videos.
- Enhanced capabilities for virtual reality and 3D applications.
Commercial Applications: Potential commercial applications include virtual reality gaming, video production, and architectural visualization software.
Prior Art: Prior art related to this technology may include research on neural network training for video processing and graphics rendering.
Frequently Updated Research: Researchers may be exploring ways to optimize the training process for neural representations in multi-view videos.
Questions about Graphics Processor Technology: 1. How does the incremental training process improve the generation of neural representations in multi-view videos? 2. What are the potential challenges in implementing this technology in real-time applications?
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.
