Dynamic 3D Scene Rendering With Canonical Cage Radiance Fields
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Solution Overview
Problem
Generating synthetic images of dynamic scenes with fine-grained detail and control is complex and computationally burdensome, requiring manual work and large amounts of training data, and existing methods struggle with real-time operation and generalization.
Innovation Solution
A method using radiance fields and volume rendering, combined with a deformation description and physics engine data, allows for the generation of realistic, controllable dynamic scenes with fine-grained features in real-time, utilizing limited training data and machine learning models like neural networks to predict color and opacity values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional computer graphics methods with complex rigged 3D models are used, then fine-grained detail and control are achieved, but the complexity and time consumption increase significantly
Solution Approach 1:
The patent replaces the traditional mechanical rigged 3D model system with a neural radiance field system. Instead of using complex hierarchical bone structures and skinning algorithms, the invention uses learned continuous volumetric representations that are directly queried during rendering, eliminating the need for manual rigging while achieving comparable or superior fine-grained detail.
Solution Approach 2:
The patent transforms the representation parameters from discrete mesh vertices and bone transformations to continuous 5D radiance field parameters (3D position, 2D view direction). This parameter transformation enables smooth deformation and fine-grained control without the discretization artifacts of traditional mesh-based approaches.
2Reliability
If complex rigged 3D models are used for dynamic scenes, then realistic animation is achieved, but the time consumption and manual work increase
Solution Approach 1:
The patent performs preliminary learning of the radiance field from training images before runtime. This pre-computation phase captures the appearance and deformation characteristics of the object, enabling fast real-time rendering without requiring complex rigged models or manual animation setup during execution.
Solution Approach 2:
The patent creates a learned copy of the object's appearance and deformation behavior in the form of a neural radiance field. This learned representation captures the essential visual characteristics without requiring the original complex 3D model structure, enabling realistic animation through direct querying of the learned field.
3Manufacturing precision
If traditional methods are used to generate synthetic images, then fine-grained detail is captured, but real-time operation becomes difficult
Solution Approach 1:
The patent replaces the computationally intensive mechanical ray-tracing and texture-mapping operations with direct neural network queries. The radiance field network directly predicts color and opacity values for any given 3D position and view direction, eliminating the need for complex per-pixel rendering calculations while maintaining fine-grained detail.
Solution Approach 2:
The patent performs the computationally heavy learning phase offline before runtime, capturing all the fine-grained appearance details in the trained neural network weights. During real-time operation, only lightweight network inference is required, enabling both high detail and fast rendering speeds.
Data Source
AI summary
A cage of primitive 3D elements and associated animation data is received. Compute a ray from a virtual camera through a pixel into the cage animated according to the animation data and compute a plurality of samples on the ray. Compute a transformation of the samples into a canonical cage. For each transformed sample, query a plurality of learnt radiance field parameterizations, each learnt on a different deformed state of the 3D scene to obtain color values for each learnt radiance field. For each transformed sample, query a learnt radiance field parameterization of the 3D scene to obtain an opacity value. Compute, for each transformed sample, a weighted combination of the color values, wherein the weights are related to the local features. A volume rendering method is applied to the weighted combinations of the color and the opacity values producing a pixel value.