Incremental Neural Representation for Dynamic Free-Viewpoint Video
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Solution Overview
Problem
Existing techniques for generating neural representations for dynamic 3D scenes are inefficient due to long training times and large data requirements, making them unsuitable for long videos or streaming applications.
Innovation Solution
An incremental neural representation method is employed, where a neural network is trained per frame, with incremental weight updates being small and compressible, allowing for efficient generation of neural representations for long videos and streaming media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques are used to generate neural representations for dynamic 3D scenes, then the neural representation can be trained to encode structural and color information, but the training time becomes very long and large amounts of data are generated
Solution Approach 1:
The patent segments the video into multiple frames and processes each frame independently through the neural network. By dividing the continuous video stream into discrete frame units, the system can generate neural representations incrementally frame-by-frame rather than processing the entire video at once, significantly reducing training time while maintaining encoding precision
Solution Approach 2:
The patent pre-trains the neural network on static 3D scenes before applying it to dynamic video sequences. This preliminary training establishes a baseline model that can then be efficiently adapted to video data, reducing the additional training time required for dynamic scenes while preserving encoding capabilities
2Measurement precision
If existing techniques are used to generate neural representations for dynamic 3D scenes, then the neural representation can be trained to encode structural and color information, but large amounts of data are generated
Solution Approach 1:
The patent extracts only the essential structural and color information from each video frame through the neural network, rather than processing and storing all raw video data. By taking out only the relevant features needed for free-viewpoint video generation, the system maintains encoding precision while dramatically reducing the volume of data that needs to be processed and stored
Solution Approach 2:
By segmenting the video into individual frames and processing them separately, the system generates manageable amounts of data per frame rather than handling the entire video as one large dataset. This segmentation approach reduces overall data volume while preserving the necessary information for high-quality reconstruction
3Adaptability or versatility
If existing techniques are used to generate neural representations, then the model can handle complex 3D scenes, but the training time becomes prohibitively long for long videos or streaming applications
Solution Approach 1:
The patent applies segmentation by processing video frames in parallel batches rather than sequentially. Each frame is independently fed through the neural network, allowing for parallel computation that significantly increases processing speed while maintaining the model's ability to handle complex 3D scenes
Solution Approach 2:
The patent uses a simplified version of the neural network for video processing compared to the full model used for static scenes. By applying partial action with a streamlined architecture optimized for video data, the system maintains adequate scene handling capability while achieving the processing speeds necessary for long videos and streaming applications
Data Source
AI summary
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.