Neural 3D Volume Streaming With Triplane Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D video conferencing systems require specialized hardware and network infrastructure, leading to high upfront costs and significant latency issues, making them impractical for widespread adoption.
Innovation Solution
A method involving triplane generation, normalization, tiling, and compression of video content, followed by neural rendering, which allows for high-fidelity 3D image generation without specialized hardware, using existing hardware and reducing network and rendering overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized multi-camera systems are used to capture 3D video, then high-fidelity 3D image quality is improved, but hardware cost and device complexity increase significantly
Solution Approach 1:
The patent creates a digital copy (neural 3D model) of the scene from 2D video inputs, replacing the need for physical 3D capture hardware. The neural field model serves as a computational replica that can be rendered from any viewpoint, eliminating expensive multi-camera rigs while maintaining visual fidelity.
Solution Approach 2:
The patent replaces mechanical 3D capture systems (multiple physical cameras positioned in space) with a computational approach using neural networks. Instead of using physical optics and mechanics to capture 3D data, the system uses learned representations from 2D video to synthesize 3D views computationally.
2Measurement precision
If full 3D models are transmitted over the network, then rendering quality is improved, but network bandwidth requirements and latency increase
Solution Approach 1:
The patent extracts only the essential parameters needed for rendering from the full 3D neural field model and transmits these compressed representations over the network. Instead of sending complete model data, the system transmits condensed feature sets that can be locally reconstructed and rendered, reducing network payload while maintaining visual quality.
Solution Approach 2:
The patent transforms the 3D spatial data into a different dimensional representation (neural field parameters in latent space) that is more efficient for transmission. By encoding the 3D scene information in a compressed parameter space rather than explicit geometric data, the system reduces the amount of data that must be transmitted while preserving rendering capability.
3Measurement precision
If high-resolution 3D models are generated, then image fidelity is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent performs preliminary processing by pre-training neural networks to extract essential scene features and pre-computing certain aspects of the 3D representation during offline phases. This allows the system to have head-start computations that reduce real-time processing requirements, enabling high-fidelity rendering with lower latency during actual video conferencing.
Solution Approach 2:
The patent implements dynamic rendering where the level of detail and processing intensity adapts based on current requirements. The system can adjust the fidelity of 3D model generation and rendering in real-time, allocating computational resources dynamically between different frames and viewpoints to maintain image quality while managing processing time constraints.
Data Source
AI summary
At least one embodiment is directed towards a computer-implemented method for training generative artificial intelligence (AI) models. The computer-implemented method includes the steps of receiving a plurality of training images; rendering, via a generative AI model, a plurality of synthetic images based on the plurality of training images; generating triplane loss metrics for the plurality of synthetic images by comparing the plurality of synthetic images against the plurality of training images; generating total variation (TV) loss metrics based on the triplane loss metrics; generating triplane compression loss metrics based on the triplane loss metrics; generating total loss metrics based on the TV loss metrics and the triplane compression loss metrics; and performing at least one backpropagation operation based on the total loss metrics to update weights associated with the generative AI model to generate an updated generative AI model.