Dynamic 3D Avatar Rendering via Ray Deformation and Neural Sampling
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Solution Overview
Problem
Current machine vision systems face computational complexity and high processing times when representing dynamic 3D objects from 2D observations, particularly in handling non-rigid deformities and topological changes, which are inaccurately represented by existing Light Field Networks.
Innovation Solution
A method involving deformation of rays using a first neural network, sampling points from the deformed rays, combining these points with a hyperspace code, and inputting them into a third neural network to obtain RGB values for rendering dynamic 3D scenes, significantly reducing computational integrations and improving processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel neural network processing is used to transform 2D objects into 3D representations, then representation accuracy is improved, but rendering time increases significantly
Solution Approach 1:
The patent segments the continuous pixel-by-pixel processing into discrete ray-based sampling. Instead of processing every pixel continuously, the system divides the scene into discrete rays that are sampled at specific points, reducing the total number of processing operations while maintaining representation accuracy.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the scene through ray sampling rather than exhaustive pixel-by-pixel processing. By selecting representative sample points along rays, the system achieves adequate representation with reduced computational effort.
2Measurement precision
If comprehensive neural network processing is applied to capture dynamic object transformations, then representation quality is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for 3D representation by processing rays at sampled points rather than processing all pixel data. This extraction approach captures the critical transformation information while eliminating redundant computational operations.
Solution Approach 2:
The patent transitions from 2D pixel-space processing to 3D ray-space processing. By formulating the problem in terms of rays traversing 3D space and sampling points along these rays, the system achieves more efficient representation of dynamic transformations by leveraging the additional spatial dimension.
Data Source
AI summary
In an example, a method may include deforming a first ray associated with a dynamic object at a first time using a first neural network and a latent code to obtain a deformed ray. The method may also include obtaining a hyperspace code associated with the first ray by inputting the first ray, the first time, and the latent code into a second neural network. The method may further include sampling one or more points from the deformed ray. The method may also include combining the sampled points and the hyperspace code into a network input. The method may further include inputting the network input into a third neural network to obtain RGB values for rendering images of a three-dimensional scene representative of the dynamic object at a second time.